ICEPAK 13.0: buone notizie per i progettisti elettronici ... - EnginSoft

Combined 1D & 3D CFD approach 

for GT Ventilation System analysis 

Analisi di un meccanismo 

link-drive per presse con tecnologia 

multibody in ANSYS 

CALL FOR PAPERS 

IS NOW OPEN 

Multi Variate Analysis in 

Systematic Impeller Design Applying 

modeFRONTIER at Sulzer Pumps 

Year 8 n°1 Spring 2011 

ICEPAK 13.0: 

buone notizie per i 

progettisti elettronici 

CAE-based 

tablet design 

Interview with 

Paolo Nesti, 

Piaggio Group 

Ottimizzazione 

Termofluidodinamica 

di un forno da 

cucina Indesit

EnginSoft Flash 

With this 1st edition of the Newsletter in 

2011, we extend a Special Invitation to 

our readers, to meet us at the EnginSoft 

International Conference 2011 from 20th 

- 21st October in Verona. There could 

hardly be a better venue for the community 

of simulation and VP (Virtual Prototyping) 

users than Verona. A UNESCO world 

heritage site, famous for its operas, the 

ancient amphitheatre built by the Romans, 

Romeo and Juliet, and a diverse cultural 

wealth. Today, Verona is a vibrant city 

dedicated to innovation. Verona’s airport 

offers daily nonstop flights to Europe’s 

hubs which will facilitate travel for our 

guests from around the world! 

The Conference, which is one of the major 

Get-togethers for CAE and VP users worldwide, will again 

present a parallel event: the ANSYS Italian Users’ Meeting. 

EnginSoft is delighted to collaborate with ANSYS, Inc. and 

ANSYS Italia, our key partners, to offer an interactive 

platform to the ANSYS developers and users to share 

knowledge, experiences and to enhance the use of ANSYS 

in the various industrial fields. 

In this edition, we report on the progress of the EnginSoft 

Americas Project. EnginSoft has recently strengthened its 

North American operations by expanding its base in Palo 

Alto, Silicon Valley. Moreover, EnginSoft has joined the 

TFSA (Thermal and Fluid Sciences Affiliate) Program of 

Stanford University. 

Cascade Technologies, Inc, EnginSoft’s partner, is a spinoff 

of the Center for Turbulence Research at Stanford 

University. Cascade develops and supports state of the art 

CFD analysis tools for various engineering applications. To 

stimulate the discussion on optimization, EnginSoft and 

Cascade have sponsored a One-Day Seminar on 

Optimization, which was held on 1st February at Stanford 

Campus. The driving force behind Cascade Technologies is 

Prof. Gianluca Iaccarino, who was recently awarded the 

Presidential Early Career Award for Scientists and 

Engineers (PECASE) by President Barack Obama. Our 

readers will find more information on Prof. Iaccarino’s 

work and the prestigious award in this issue. 

EnginSoft’s growing international business is also 

reflected in the highlights of this issue: Sulzer Pumps, one 

of the world's leading centrifugal pump manufacturers 

based in Winterthur, Switzerland, speaks about Multi 

Variate Analysis in Systematic Impeller Design. 

Ing. Stefano Odorizzi 

EnginSoft CEO and President 

Newsletter EnginSoft Year 8 n°1 - 3 

Researchers of Trinity College Dublin have 

significantly improved the fatigue resistance 

of components using modeFRONTIER. GE Oil 

& Gas Italy adopted a combined 1D and 3D 

numerical approach with Flowmaster and 

ANSYS Fluent to study ventilation systems. 

We interviewed Mr Paolo Nesti, engineer at 

Piaggio Group, one of the major players 

worldwide in the two-wheeler vehicles 

sector, and feature a case study on the use of 

ANSYS Workbench at Piaggio. Landi Renzo 

S.p.A., a global leader in components, LPG 

and CNG fuel systems for motor vehicles, 

spoke to us about the use of modeFRONTIER 

in their product development. Componeering 

Inc. Finland presents the Opencell Delta 

concept which provides a brand new way to 

construct metal sandwich panels. 

The Event Calendar features conferences, fairs and courses 

across Europe, in the USA and Japan... 

When we hear about Japan in these days, above all our 

heart and best wishes go out to the Japanese people who 

battle and will overcome the consequences of a terrible 

natural disaster that hit their country. Our Japan Column 

brings to our readers an article on the novel approach of 

CAE-based tablet design of Mr. Hideaki Sato of ASAHI 

BREWERIES, LTD. Elysium presents news on the use of 

ASFALIS at Nissan. We close the Column with an article on 

Tokyo, a unique metropolis…and some ideas on how each 

one of us can help. 

Finally, the Editorial Team would like to recommend the 

new book “Reactive Business Intelligence. From Data to 

Models to Insight” by Prof. Roberto Battiti and Prof. 

Mauro Brunato to our readers. While the book is easy-toread, 

it guides us from the very basics to such advanced 

topics as supervised learning, data-mining, optimization, 

statistics and interactive visualizations. 

To continue our discovery of the immense opportunities of 

CAE and VP in our today’s world, EnginSoft and ANSYS 

invite our readers to the International Conference 2011. 

Please follow the Announcements, Call for Papers and 

Program on www.caeconference.com 

We look forward to welcoming you to Verona this October! 

Stefano Odorizzi 

Editor in chief

4 - Newsletter EnginSoft Year 8 n°1 

Sommario - Contents 

CASE STUDIES 

6 Multi Variate Analysis in Systematic Impeller Design Applying modeFRONTIER at Sulzer Pumps 

10 Analisi di un meccanismo link-drive per presse con tecnologia multibody in ANSYS 

13 Ottimizzazione termofluidodinamica di un forno da cucina Indesit 

15 Combined 1D & 3D CFD Approach for GT Ventilation System Analysis 

19 ANSYS WB and a Review of the Design Metrics in Piaggio: the Case of the Motor Shaft 

INTERVIEWS 

21 EnginSoft Interviews Ing. Paolo Nesti, Piaggio Group 

CASE STUDIES 

26 modeFRONTIER Used in the Design of Fatigue-Resistant Notches 

27 A Multi-Objective Optimization with Open Source Software 

SOFTWARE NEWS 

32 ANSYS 13: Il punto sui solutori per modelli di grandi dimensioni nelle simulazioni meccaniche 

33 La simulazione di sistema in ANSYS: Simplorer 

36 ICEPAK 13.0: buone notizie per i progettisti elettronici 

38 Development of the Novel Opencell 

IN DEPTH STUDIES 

41 Componenti forgiati di qualità necessitano di un approccio CAE integrato – esperienze di simulazione di processo 

nel campo Energia e Nucleare 

TESTIMONIAL 

46 Landi Renzo: the global leader in the sector of components and LPG and CNG fuel systems 

JAPAN CAE COLUMN 

47 The CAD-CAM Cooperation in Nissan Achieved by ASFALIS 

48 CAE-based tablet design 

52 Tokyo a Metropolis 

The EnginSoft Newsletter editions contain references to the following 

products which are trademarks or registered trademarks of their respective 

owners: 

ANSYS, ANSYS Workbench, AUTODYN, CFX, FLUENT and any and all 

ANSYS, Inc. brand, product, service and feature names, logos and slogans are 

registered trademarks or trademarks of ANSYS, Inc. or its subsidiaries in the 

United States or other countries. [ICEM CFD is a trademark used by ANSYS, 

Inc. under license]. (www.ANSYS.com) 

modeFRONTIER is a trademark of ESTECO srl (www.esteco.com) 

Flowmaster is a registered trademark of The Flowmaster Group BV in the 

USA and Korea. (www.flowmaster.com) 

MAGMASOFT is a trademark of MAGMA GmbH. (www.magmasoft.com) 

ESAComp is a trademark of Componeering Inc. 

(www.componeering.com) 

Forge and Coldform are trademarks of Transvalor S.A. 

(www.transvalor.com) 

AdvantEdge is a trademark of Third Wave Systems . 

(www.thirdwavesys.com) 

LS-DYNA is a trademark of Livermore Software Technology Corporation. 

(www.lstc.com) 

SCULPTOR is a trademark of Optimal Solutions Software, LLC 

(www.optimalsolutions.us) 

Grapheur is a product of Reactive Search SrL, a partner of EnginSoft 

For more information, please contact the Editorial Team

PRESS RELEASE 

54 President Obama Honors EnginSoft’s Partner with the 

Presidential Early Career Award for Scientists and 

Engineers 

56 Formazione a distanza sugli elementi finiti 

BOOK REVIEWS 

57 REACTIVE BUSINESS INTELLIGENCE: From Data to 

Models to Insight 

EVENTS 

58 EnginSoft at the Optimization Day: Research and 

Applications 

60 NAFEMS World Congress 2011 - Preliminary Agenda 

Announced 

61 EnginSoft alla Fiera Made in Steel di Brescia 

62 EnginSoft Event Calendar 

PAGE 6 MULTI VARIATE ANALYSIS IN 

SYSTEMATIC IMPELLER DESIGN APPLYING 

MODEFRONTIER AT SULZER PUMPS 

PAGE 15 COMBINED 1D & 3D CFD 

APPROACH FOR GT VENTILATION 

SYSTEM ANALYSIS 

PAGE 36 ICEPAK 13.0: BUONE NOTIZIE 

PER I PROGETTISTI ELETTRONICI 

Newsletter EnginSoft 

Year 8 n°1 - Spring 2011 

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Multi Variate Analysis in 

Systematic Impeller Design Applying 

modeFRONTIER at Sulzer Pumps 

The most important pump component - its heart - is the 

impeller which transforms kinetic energy into pressure and 

therefore generates the required head. The impeller 

geometry is defined by more than 50 parameters requiring 

experienced hydraulic design engineers. Even if only 20 of 

these parameters have a major influence, it is obvious 

that a severe variation yields an excessive database which 

should be made use of. 

Fig. 1 - Two stage pump with detailed view of the first stage impeller. 

Fig. 2 - Dimensions of the impeller. 

A proper classification of the available designs in the 

database gives the developer a better understanding of 

the complex parameter correlation and enables the 

prediction of not yet available impellers by interpolating 

among the existing designs. This gives a first parameter 

estimate for the new impeller and properly conditions the 

variable ranges for an optimization which is likely to 

follow. This article presents an approach based on 

classification of existing impeller designs with Multi 

Variate Analysis through Self Organizing Maps (SOM) by 

use of modeFRONTIER. 

Systematic impeller design 

The parameters defining an impeller include the main 

dimensions like outer diameter D2 and shaft diameter D0 as 

also the meridional contour and blade shape (Figure 2). 

The impeller design is done for a specified operating point 

with given flow rate Q and head H for a certain rotational 

speed n. Efficiency η is one criterion for an optimal 

impeller design not only at best efficiency point bep but 

also over a certain operating range (Figure 3, left). 

Suction capability, which is the pressure available at pump 

inlet NPSH, is another criterion (Figure 3, centre). 

Decreasing NPSH affects the pump head which needs to be 

considered. 

Good suction capability and high efficiency both over a 

broad operating range are conflictive design goals. 

Increasing suction capability at maximum operating point 

reduces efficiency at minimum operating point. This is an 

important fact when using optimization techniques in 

impeller design. 

Characteristic numbers 

Impellers can be classified by characteristic numbers 

enabling a comparison among the designs. The specific 

speed nq defines the form of the impeller (Figure 4) and is 

calculated from flow rate Q, head H and the rotational 

speed n. Figure 5 lists the main design parameters and 

shows their conversion into characteristic numbers. The 

outer diameter D2 of the impeller is selected according an 

Fig. 3 - left: Efficiency η; centre: suction capability NPSH within the operating range of the impeller dependent on the flow rate Q; right: NPSH3% criterion

optimal head coefficient Ψ for the specific speed nq. The 

inlet diameter D1 influences the suction capability and 

depends on the flow coefficient at inlet ϕ1. The suction 

capability can either be expressed by a characteristic 

number σ or the suction specific speed nss which both 

depend on the suction head at pump inlet NPSH. Similar 

relations exist for other dimensions. 

Using these characteristic numbers and dimensionless 

values, impellers with different outer diameters D2 can be 

Fig. 4 - Impeller form in meridional view dependent on specific speed nq 

compared and new designs can be calculated based on 

these values. This facilitates a classification of the 

impellers and the use of the SOM technique. 

Self-Organizing Map 

Any existing impeller design is described through a multidimensional 

vector, where each component represents a 

defining parameter (input) or a performance index 

(output). 

The Self-Organizing Map (SOM) is an unsupervised Neural 

Network algorithm capable to group and classify such 

already available impeller designs in a two-dimensional 

grid space. Each node of this grid is called “Unit” and it 

groups (includes) vectors (impeller designs) 

that are similar with respect to all their 

parameters (inputs and outputs) 

simultaneously. 

SOM preserves the topology of the data, so 

that similar data items will be mapped to 

nearby units on the map. To do so, units are 

hexagonal-shaped, and hence each unit has 6 

neighbors and is labeled by a “prototype 

vector” that in fact represents all the vectors 

included in the unit itself, as a kind of 

average. 

SOM lives in the multi-dimensional data 

space, but its visualization capabilities are 

built on the top of its representation in the 

grid space. Each of the hexagons becomes a 

Fig. 5 - Correlation between dimensional and non dimensional values 


“pixel” being colored to reflect different properties of the 

input data, e.g. specific speed n q, impeller width B2 or 

efficiency η. This way SOM overcomes the problem of 

visualizing multivariate data: input data are projected 

onto a 2D grid (Figure 6). 

Another advantage of the SOM is related to its intrinsic 

interpolation capability. There might exist regions of 

unexplored zones, the performance of the impeller is not 

yet predicted by CFD. This is reflected in SOMs by empty 

hexagons separating others that are filled up by different 

families of designs. When this happens, it is reasonable to 

look at the prototype vector of the empty unit as kind of 

forecast of a design family that have still to be realized, 

and that represent a reasonable interpolation of the ones 

that are available. The benefit of the SOM is that, apart 

from the input variables characterizing such design 

families, also a complete forecast of the performances is 

immediately available. This predictive use of the SOM is 

really powerful when handling designs that are described 

by a high number of parameters (inputs and outputs) so 

that any other interpolations approach, like Response 

Surface Modeling, becomes heavy to implement. 

For the pump impellers described here, a unique SOM is 

trained on the existing database and used to forecast new 

design families able to provide certain performances. 

Impeller design and multi variate analysis 

The dimensionless values for the main parameters and the 

design objectives efficiency and suction capability (η, 

nSS,bep, nSS,max) are selected as input for the training 

(classification) of the SOM. Within this test case, the 

results of six different impeller optimizations with three 

Fig. 6 - Left: SOM is the blue network that adapts to real data (red points) in a X-Y-Z space, 

note that some nodes are far from any real point, hence the related unit will be empty. 

Centre: the same SOM in its 2D conventional representation: each of the nodes is a hexagonal 

unit. Right: each unit is colored with respect to the X-value of its prototype vector, and the 

square on its center represents the number of real design enclosed in the unit (see the empty 

units).


Fig. 7 - Specific speed (nq) in the SOM with distribution of existing 

designs 

different specific speeds between nq13 and nq60 are used 

as data basis. Goal of this study is to develop new 

impellers in this range with high suction capability and 

high efficiency for four different specific speeds (n q16, 

n q24, n q30, n q47) under the assumption that for each n q 

both operating point and impeller diameter D 2 are given 

and the shaft diameter is pre-defined from mechanical 

calculations. 

Figure 7 illustrates the trained SOM of the specific speed 

ranging from n q13 (blue) to n q60 (red). The squares 

describe the density of input parameters available. The 

larger the square the more data exists, no square signifies 

that parameter values are based on pure interpolation. 

The selection of the new impellers is undertaken in 

regions with purely interpolated data (Figure 8). For each 

new impeller, existing designs with a similar n q in the SOM 

table are compared. This is necessary as three objectives 

need to be fulfilled, and the SOM designs might only 

achieve one. 

The advantage of this technique is the access to every 

single parameter defining the impeller geometry. With an 

amount of over 50 parameters, the entire meridional 

impeller contour and the blade shape are approximated by 

the SOM. This method allows a complete impeller design 

within a few minutes just by giving the operating point 

and selecting an appropriate outer diameter D 2. The 

impeller parameters are taken from the SOM and converted 

Table 1 - Comparison of coefficients of obtained and predicted objectives for the 

selected designs 

Fig. 8 - Selected designs in the SOM (Color: specific 

speed) 

back from dimensionless 

to dimensional 

parameters. A new 

impeller is then generated 

with the conventional 

design tools and its 

performance is checked by 

CFD. 

Table 1 shows a 

comparison of the 

performances obtained by 

CFD and predicted by SOM 

for the selected nq. A 

coefficient is defined 

with: 

objectiveCFD / objective SOM , 

describing the ratio of the CFD result to the prediction of 

the objective by the SOM. A value equal to one signifies 

an error of zero; the CFD performance matches the 

predicted one. For a coefficient smaller than one, the 

performance is over predicted, if it is larger than one, the 

design is under predicted by the SOM. 

n q24 

The first impeller modeled with the SOM is n q24. Therefore 

the best possible solution in compliance with the specific 

speed and the design goals is selected. CFD calculations 

are performed according the CFD in the optimization 

process. The results are excellent, both efficiency and 

suction performance are better than predicted by the SOM. 

n q16 

For the second impeller (n q16) two different designs are 

selected from the SOM table, one with high efficiency 

predicted and a second with a lower efficiency but a 

better expected suction capability. The impeller with high 

efficiency reaches almost the suction capability at bep 

while the impeller with the lower efficiency exceeds the 

suction capability. Both impellers outperform the 

expected suction capability at the maximum operating 

point efficiency. 

n q30 

For the impeller n q30 two different interpolated designs 

are selected from the SOM table, differing in suction 

capability at maximum operating point. After 

calculating performances of the interpolated designs, 

the aspired suction capability at overload is not 

achieved, the other targets are outperformed. For this 

reason two more designs from the SOM table are 

selected, now with lower efficiency than the previous 

designs. With the lower efficiency target, the suction 

capability is reached for both operating points. This 

proves the conflicting design targets efficiency and 

suction capability.

Fig. 9 - Efficiency (red = high, blue = low) Fig. 10 - suction capability at bep (based on σ, 

blue = good, red=bad) 

n q47 

Two designs from the SOM table are selected. The second 

design with the higher suction capability target at bep 

misses the required suction capability at maximum 

operating point. Even if the first design does not fulfill 

the requirements at bep, the deviation from the target 

value is only small. 

Summary for all designs 

For all designs, the calculated efficiency is higher than 

SOM predicted. The suction capability misses the 

requirements for some designs because of contradicting 

objectives. In these cases it is possible to select new 

designs from the SOM with compromises in efficiency but 

achieving the required suction capability. 

Figures 9-11 present the objectives efficiency, suction 

capability at bep and max OP. It can be clearly seen that 

efficiency and suction performance pattern are completely 

different. This discrepancy makes it difficult to fulfill all 

three objectives and either a compromise is required or 

one objective has to be prioritized. 

Conclusions 

The article describes a novel methodology to design 

impellers starting from the well assessed knowledge at 

Sulzer Pumps 

Sulzer Pumps is one of the world's leading centrifugal 

pump manufacturers. Intensive research and development 

in fluid dynamics, process-oriented products and special 

materials as well as reliable service helps Sulzer Pumps 

maintain its leading positions in its key markets. Its 

customers come from the oil and gas, hydrocarbon 

processing, power generation and pulp and paper sectors 

as well as from water distribution and treatment and 

other general industries. The products are internationally 

reputed for their technical excellence. 

www.sulzerpumps.com 


Fig. 11 - suction capability at max OP (based on σ, 

blue = good, red=bad) 

Sulzer Pumps. The concept has proven to provide the 

designer a new and effective tool to speed up the design 

process of the pumps core part - the impeller. 

Self-Organizing Maps (SOM) have been trained on the 

already available impeller designs and corresponding 

performance indicates: such a SOM embeds the so far 

available pump designer knowledge, and provides a 

complete interpolation in regions in which designs are 

still missing. Each input or output variable of the impeller 

design can be represented through a conventional twodimensional 

SOM map. This allows the use of SOM as an 

extremely powerful tool to suggest new designs in regions 

in which the design space has not yet been explored to 

forecast their performances. In fact, the methodology 

allows a new and complete impeller design in some 

minutes, just assigning a few parameters, as the operating 

point and the outer diameter. Any new design proposed by 

the SOM can be validated through high-fidelity fluid 

dynamic simulations (CFD) and then be used as starting 

point for further refinements by directly linking the CFD 

model to a modeFRONTIER optimizer, [2]. 

References 

[1] Gülich J.: "Centrifugal Pumps", Springer, 2010 

[2]Krüger S., Maurer W.: "How to use modeFRONTIER 

within the daily hydraulic design process: Sulzer 

Pumps’ experiences with automated impeller design", 

modeFRONTIER 2008 Users’Meeting, Trieste, 14th-15th 

October 2008 

Wolfgang Maurer, Susanne Krueger 

Sulzer Pumps, Winterthur, Switzerland 

Luca Fuligno 

EnginSoft SpA, Trento, Italy 

Francesco Linares 

EnginSoft GmbH, Frankfurt am Main, Germany


Analisi di un meccanismo link-drive per 

presse con tecnologia multibody in 

ANSYS 

Analysis of a link drive mechanism for presses using ANSYS 

MBD multibody technology 

This paper presents a test case in which the tool “Rigid 

Dynamics” of ANSYS Workbench 13 is used to investigate the 

kinematics and the dynamics of a link drive mechanism 

equipping a deep drawing press. 

The device is first analyzed by developing the kinematic motion 

equations. This step highlights the difficulty which arises when 

we have to manually manipulate the equations of a complex 

multibody system. 

Then, a parameterized multibody model of the link drive is built 

in ANSYS. This approach is much more straightforward and 

allows the user to understand the mechanism’s behavior in a 

shorter time. In addition, the software makes it possible to 

watch the working mechanism animation at the end of the 

solution. 

The multibody model returns information about the dynamics, 

which is useful for structural design purposes. Moreover, thanks 

to the easy parameter management in ANSYS, we can 

automatically investigate and compare multiple alternatives of 

the same mechanism. 

Questo articolo presenta un test case significativo nel quale attraverso 

una “Rigid Dynamics” di ANSYS Workbench 13 vengono 

efficacemente analizzate la cinematica e la dinamica di una 

pressa meccanica per imbutitura profonda. 

L’imbutitura è un processo di formatura a freddo attraverso il 

quale una lamiera metallica viene trasformata in un oggetto cavo, 

con buone caratteristiche dimensionali e di finitura. Nello 

schema tradizionale, l’imbutitura si realizza attraverso un pun- 

Fig. 1 – confronto tra pressa “Slider–Crank”e “link drive”: 

velocità del punzone e zona di lavoro 

zone che spinge la lamiera, eventualmente fissata con un premilamiera, 

all'interno di una matrice. Il processo è intrinsecamente 

delicato perché deve indurre nel materiale elevate deformazioni 

plastiche, senza raggiungere la condizione di rottura. 

La qualità del prodotto finale è fortemente influenzata dai parametri 

di processo, tra i quali spicca per importanza la velocità 

di discesa del punzone nel tratto di corsa in cui lavora la lamiera. 

Idealmente, la velocità del punzone dovrebbe essere bassa, per 

realizzare una deformazione graduale del materiale, e costante, 

per evitare la formazione di pieghe e striature superficiali. 

In una pressa meccanica con tradizionale schema slider crank, 

la riduzione della velocità del punzone è ottenibile solo aumentando 

il tempo ciclo, con ovvie conseguenze negative sulla 

produttività dell’impianto. Pertanto, se si vogliono ottenere 

buone performance di processo senza penalizzare la produzione, 

è opportuno predisporre un meccanismo più raffinato, che 

consenta maggiori libertà nella gestione della velocità del punzone. 

Una soluzione è il meccanismo link drive illustrato, già 

utilizzato da alcuni produttori di presse. La Figura 1 confronta 

le curve di velocità del punzone per una pressa tradizionale e 

una pressa link Drive di pari dimensioni (con la stessa corsa 

massima e lo stesso tempo ciclo). 

La velocità di discesa del punzone, per la pressa link drive, presenta 

un tratto con andamento regolare a velocità quasi costante. 

Inoltre, grazie al maggior numero di membri, questo 

meccanismo è molto versatile: variando le dimensioni del meccanismo 

si possono ottenere diverse curve di velocità. Risulta, 

pertanto, evidente che il link drive presenta caratteristiche e 

prestazioni molto più vantaggiose dello schema tradizionale. 

Studio analitico del meccanismo link drive 

Il paragrafo precedente ha messo in luce l’importanza della velocità 

di discesa del punzone nella messa a punto del processo 

di imbutitura. Pertanto, è opportuno focalizzare l’attenzione 

sull’analisi cinematica del meccanismo link drive, che permette 

di comprendere come il meccanismo trasformi la rotazione del 

motore elettrico nella traslazione a velocità variabile del punzone. 

Gli approcci per condurre uno studio cinematico sono diversi. 

Per un meccanismo relativamente semplice come il link drive, 

è possibile, seppure con qualche difficoltà, derivare analiticamente 

le equazioni del moto. Lo schema cinematico cui si farà 

riferimento è riportato in Figura 2.

Fig. 2 – schema cinematico della pressa link drive 

I vettori L1, L3, L4, L5 e L6 rappresentano i membri mobili del 

meccanismo, il vettore L2 rappresenta il telaio e il vettore srappresenta 

la posizione del punzone (misurata da un riferimento 

arbitrario). Lo studio analitico della cinematica inizia con la 

scrittura delle equazioni di chiusura in forma vettoriale: 

Ciascun vettore può essere formalmente descritto nella forma 

L= L cos (φ), dove L è la lunghezza e φ è l’angolo di inclinazione. 

Sostituendo nelle precedenti e manipolando opportunamente 

è possibile esprimere la posizione s del punzone in funzione 

dell’angolo φ4 di rotazione dell’eccentrico (variabile indipendente 

del problema). Successivamente, si ricavano per derivazione 

rispetto al tempo la velocità v e l’accelerazione a. In 

sintesi, l’analisi cinematica mediante approccio analitico restituisce 

tre equazioni nella forma: 

Queste espressioni hanno una struttura molto articolata e pertanto 

ne omettiamo la scrittura estesa. Si noti che i risultati 

dipendono dalla legge di moto assegnata al movente e dai parametri 

dimensionali del meccanismo. 

Benché l’approccio analitico consenta di pervenire ai risultati 

cinematici in tempi accettabili, va precisato che la manipolazione 

di equazioni con questo livello di complessità è una operazione 

alquanto delicata: se non si dispone di strumenti per la 

manipolazione simbolica, il rischio di errore è decisamente elevato. 


Simulazione multibody del meccanismo link drive 

Una valida alternativa per studiare meccanismi in modo più veloce 

e con minor rischio di errore è la simulazione tramite codice 

multibody. ANSYS Workbench 13, attraverso il modulo 

“Rigid Dynamics”, consente di creare e gestire modelli 

multibody a corpi rigidi. L’utilizzo di questo strumento, permette, 

inoltre, di integrare i risultati dell’analisi cinematica con 

tutte le grandezze dinamiche fondamentali per la progettazione 

strutturale del dispositivo. 

ANSYS Workbench 13 consente di gestire in modo parametrico 

qualsiasi modello di calcolo. Con riferimento all’analisi 

multibody del link drive, la parametrizzazione permette di simulare 

varie alternative del meccanismo, consentendo la scelta 

di quella più adatta alle esigenze. 

Nella fase di pre-processing avviene l’assemblaggio del modello 

multibody. Le geometrie dei corpi possono provenire da CAD 

esterni oppure possono essere create direttamente all’interno 

di Design Modeler. Per questa applicazione abbiamo provveduto 

a creare integralmente la pressa ed il meccanismo, parametrizzando 

le grandezze di cui andremo ad analizzare gli effetti. 

Sono quindi state scelte e create le connessioni tra i corpi 

(Figura 3). 

I revolute joint consentono la rotazione relativa tra i membri 

connessi, mentre i general joint lasciano liberi i gradi di libertà 

scelti esplicitamente dall’utente. 

Per consentire la soluzione di un modello multibody a corpi rigidi, 

i vincoli, inseriti sottoforma di connessioni, non devono 

essere ridondanti. ANSYS mette a disposizione lo strumento 

“Redundancy Analysis” che permette di individuare automaticamente 

la presenza di vincoli in eccesso e che fornisce indi- 

Fig. 2 – vista 3D dell’assieme e schema delle connessioni


Fig. 4 – modelli di pressa ottenuti tramite parametrizzazione 

cazione di quali modifiche si debbono apportare per rendere il 

modello consistente. 

Il meccanismo virtuale viene azionato applicando una legge di 

moto all’albero dell’eccentrico. ANSYS consente di assegnare a 

qualsiasi connessione sia leggi di moto, sia azioni dinamiche. 

In entrambi i casi, le funzioni sono gestibili in forma tabulata 

o tramite espressioni analitiche. Nel caso del meccanismo link 

drive, abbiamo imposto al movente una velocità di rotazione 

costante. 

L’analisi multibody comporta l’integrazione delle equazioni del 

moto che il software ha sviluppato automaticamente durante 

l’assemblaggio del modello. ANSYS dispone di due integratori, 

con diverse opzioni per la gestione del passo, della convergenza 

e della qualità della soluzione. 

Nella fase di post-processing ANSYS Workbench permette di 

estrarre numerosi risultati dai corpi e dalle connessioni. Le 

grandezze disponibili per i corpi sono di natura cinematica (posizione, 

velocità, accelerazione), mentre per le connessioni 

possiamo diagrammare le grandezze cinematiche dei gradi di libertà 

consentiti e le reazioni vincolari dei gradi di libertà annullati 

dal joint. 

Ad esempio, su un “revolute joint” possiamo leggere angolo, 

velocità ed accelerazione angolare lungo l’asse di rivoluzione, 

e, in aggiunta, possiamo estrarre forze radiali, forze assiali e 

momenti trasversali che i corpi si scambiano mutuamente. 

Fig. 5 – confronto della velocità del punzone per i tre modelli di pressa 

Confronto di 3 configurazioni del meccanismo link drive 

A titolo di esempio, proponiamo lo studio comparativo della risposta 

cinematica restituita dal meccanismo link drive modificando 

la coordinata orizzontale della cerniera di collegamento 

della biella L1 al telaio (Figura 2 e Figura 4). 

Nello specifico, abbiamo ipotizzato di passare da un valore di 

1400 mm a 1100 mm, con un valore intermedio di 1250 mm. 

Le tre analisi sono condotte semplicemente modificando il parametro 

corrispondente nell’interfaccia utente. ANSYS MBD aggiorna 

automaticamente le geometrie e ripete la simulazione 

multibody. 

La Figura 5 illustra gli effetti del parametro scelto sulla velocità 

del punzone. Lo zoom mette in particolare evidenza la comparsa 

del tratto a velocità quasi costante, passando dalla pressa 

A alla pressa C. Pertanto, se l’obiettivo è quello di usare la 

pressa per un processo di imbutitura, la soluzione C è la migliore. 

Naturalmente, sfruttando la parametrizzazione del modello, 

è possibile individuare configurazioni con prestazioni ulteriormente 

migliorate. 

Per maggiori informazioni: 

Fabiano Maggio - EnginSoft 

info@enginsoft.it 

L’esempio del meccanismo link drive solleva una serie di 

problematiche tipiche della modellazione multibody. Infatti, 

l’utente deve scegliere con cura numero e tipologia di vincoli 

se non vuole pervenire a risultati incompleti o addirittura 

errati. L’utilizzo di strumenti come “ANSYS Transient 

Structurla MBD” presuppone che l’utente possieda adeguate 

nozioni di meccanica applicata e calcolo numerico che gli 

consentano di tradurre correttamente un sistema fisico in un 

modello virtuale. La schematizzazione può avvenire in modo 

più o meno raffinato, con conseguenze dirette sull’efficacia 

della simulazione. È compito del modellista scegliere 

dimensione, grado di complessità e dettagli del modello che 

vuole creare, considerando simultaneamente obiettivi da 

raggiungere, onere computazionale e tempo a disposizione. 

Il miglior modello non è quello più dettagliato, ma quello 

che risponde in modo più veloce ed esauriente alle esigenze. 

Questa regola, che vale in generale per tutte le dimensioni 

del CAE, assume un ruolo decisivo per la simulazione 

multibody. 

EnginSoft propone un corso di modellistica multibody della 

durata di 2 giorni a tutti i progettisti che affrontano 

quotidianamente problemi di cinematica e dinamica. Il corso 

è pensato e strutturato in modo da trasferire in breve tempo 

le conoscenze che servono a formulare consapevolmente le 

principali scelte di modellazione multibody. Il corso verrà 

tenuto dal prof. Roberto Lot dell’Università di Padova in 

collaborazione con l’ing. Fabiano Maggio di EnginSoft. 

Per informazioni sui contenuti consultare il sito del 

consorzio TCN www.consorziotcn.it 

Per iscrizioni e informazioni generali consultare la sig.ra 

Mirella Prestini della segreteria del consorzio. E-mail: 

info@enginsoft.it Tel: 035 368711

Indesit Company è tra i leader in 

Europa nella produzione e commercializzazione 

di grandi elettrodomestici: 

lavabiancheria, asciugabiancheria, lavastoviglie, 

frigoriferi, congelatori, 

cucine, cappe, piani di cottura e forni. 

Proprio su quest’ultimo prodotto, il 

forno da cucina, si è recentemente 

concentrata un fase di sviluppo volta a 

migliorarne efficienza in termini di 

consumi e di uniformità di cottura. 

Lavorando già da tempo con strumenti 

di modellazione numerica (nello 

specifico ANSYS ICEM CFD e ANSYS CFX) Fig. 1 

per coadiuvare le prove sperimentali, 

Indesit Company ha deciso di utilizzare tali strumenti per tutta 

la fase di ottimizzazione della termodinamica interna del forno, 

avvalendosi del software di ottimizzazione modeFRONTIER e della 

collaborazione dei tecnici della EnginSoft S.p.A. 

A valle di studi sperimentali e considerazioni basate su know 

how interno, si è pensato che, per migliorare le prestazioni del 

forno dal punto di vista energetico e funzionale, l’attenzione 

maggiore doveva essere posta sulla paratia forata che si trova tra 

la ventola e la zona di cottura. Il lavoro presentato, è quindi 

quello dell’ottimizzazione geometrica di tale paratia, alla ricerca 

della configurazione tale da avere minori consumi ed una maggiore 

uniformità di temperatura nella muffola. 

Modellazione 

Si è partiti dalla modellazione di tutto il “volume bagnato” del 

forno nella configurazione baseline di partenza. Il forno, in questa 

configurazione, presenta anche una leccarda nella parte inferiore 

della zona di cottura. Sfruttando il fatto che la parte geometrica 

soggetta a modifica parametrica è la sola paratia, si è 

pensato di dividere il volume in due parti con un piano che separi 

il dominio di calcolo in prossimità della paratia. In questo 

modo, la parte di modello a valle della paratia, comprendente 

anche tutta la leccarda, rimane invariata, e viene perciò preparata 

(geometria + griglia di calcolo) una sola volta. L’altra parte 

del modello, è a sua volta suddivisa in un volume che rimane invariato, 

il dominio rotante con la ventola, e tutto il resto, comprendente 

fra gli altri la paratia, la cui geometria e griglia di calcolo 

sono state parametrizzate per poter essere rigenerate automaticamente 

di volta in volta sulla base delle scelte operate 

dal’ottimizzatore. 

Il software utilizzato per le modifiche geometriche e la generazione 

della griglia di calcolo è stato ANSYS ICEM CFD. 

Mettendo a punto una sequenza di istruzioni opportune, ICEM 

modifica la geometria della paratia e genera la griglia del volume 

ottenuto. In questo insieme di istruzioni (script) sono 

presenti oltre a tutti i parametri geometrici, anche quelli re- 


Ottimizzazione termofluidodinamica di 

un forno da cucina Indesit 

Fig. 2 

lativi alla dimensione degli elementi 

della griglia di calcolo, sia a livello 

globale che locale. Per tutte le configurazioni 

analizzate, 3 strati di prismi 

sono stati estrusi su tutte le pareti del 

dominio di calcolo. Le griglie di calcolo 

che tipicamente si possono ottenere 

con questa impostazione constano 

di circa due milioni di elementi tetraedrici 

ed un milione di prismi. È chiaro 

tuttavia che il numero di elementi è 

leggermente diverso per ogni configurazione, 

essendo la geometria parametricamente 

variata per ogni design. 

L’attività sul modello baseline, oltre 

che rappresentare il riferimento per quantificare il margine e la 

direzione del miglioramento nella fase di ottimizzazione, è servita 

anche per la fase di taratura, indispensabile in attività di 

questa portata per determinare il miglior compromesso tra numero 

di elementi, qualità degli stessi e numerica più efficace all’ottenimento 

di risultati affidabili del calcolo CFD. 

Variabili di Input ed Output 

modeFRONTIER è un ottimizzatore multi disciplinare e multi 

obiettivo. L’esperienza che vanta EnginSoft nell’utilizzo di questo 

strumento accoppiato a software di analisi numerica, ha consentito 

la messa a punto di un flusso logico di macro-operazioni 

che hanno portato ad una vera e propria “customizzazione” 

per il problema qui illustrato (ottimizzazione forno). 

I parametri geometrici di ingresso sono stati in tutto sedici. Essi 

hanno permesso di controllare dimensione, distribuzione e numero 

di fori sui quattro bordi della paratia. Lo spazio delle possibili 

configurazioni è stato delimitato in tre modi: dai valori minimi 

e massimi che ciascuna variabile di input doveva rispettare, 

da vincoli di costruzione, e da vincoli geometrici che evitassero 

geometrie degeneri. 

Discorso un po’ più dettagliato meritano le variabili di output. A 

monte del lavoro di ottimizzazione è stata valutata molto attentamente 

la modellazione delle variabili in uscita. Esse infatti devono 

rappresentare un indice sia dell’efficienza del forno in termini 

di uniformità della temperatura che del suo consumo di


energia. Si è deciso di scegliere 

come grandezza per l’uniformità 

di temperatura, il suo scarto quadratico 

medio (RMS di T) misurato 

su una nuvola di punti situata 

“ad hoc” nella zona di cottura, 

mentre per la potenza elettrica 

necessaria si è optato per la portata 

d’aria ricircolante all’interno 

della muffola. Con queste assunzioni, 

gli obiettivi implementati 

in modeFRONTIER diventano perciò 

la ricerca di un design che 

renda minimo il valore dello scarto 

quadratico medio delle temperature 

sulla nuvola di punti garantendo 

allo stesso tempo una 

portata d’aria superiore ad un va- Fig. 3 

lore limite precedentemente stabilito come limite inferiore. 

Il processo logico che lega tutti i passaggi è il seguente: 

Partendo dal file replay.rpl che modeFRONTIER ha aggiornato 

con i valori delle variabili di ingresso, lo script, eseguito 

in batch da ICEM, si occupa di modificare la paratia con i 

fori, copiare il resto della geometria che resta immutata e 

generare la griglia di calcolo. 

Terminata questa prima fase, la griglia di calcolo generata 

viene caricata assieme a quella che resta inalterata nel preprocessor 

di CFX. Al modello così aggiornato viene quindi 

applicato il setup fisico e numerico dell’analisi cfd, scritto il 

file di lancio e lanciato il run, specificando eventualmente se 

il calcolo deve essere eseguito in modalità parallela. Anche 

questa fase è interamente eseguita in modalità batch da tutti 

i software coinvolti. 

Finita l’analisi, sempre in batch, un altro script del post-processore 

di CFX, calcola le grandezza di output dal file di 

risultati, concludendo così l’iterazione per il singolo design 

Doe e Ottimizzazione 

Il DOE (Design Of Experiment) di partenza è stato realizzato mediante 

l’algoritmo RANDOM tra i più adatti per un’ottimizzazione 

mono-obiettivo. In effetti anche se gli output sono due, solo 

la minimizzazione dello scarto quadratico medio della temperatura 

è un vero e proprio obiettivo, dato che il controllo del valore 

di portata d’aria smaltita dalla ventola sia sempre superiore 

ad un valore minimo è considerato come un vincolo del problema. 

Il numero di design iniziale, dipendente anche dal tipo di 

algoritmo scelto per l’ottimizzazione, è in questo caso calcolato 

come la somma dei parametri di ingresso più uno, come richiesto 

dall’algoritmo di ottimizzazione utilizzato. 

A seguito della campagna di analisi sui risultati del DOE di partenza, 

è iniziata la fase di ottimizzazione, mediante l’utilizzo 

dell’SIMPLEX. Il numero di design simulati in questa fase, utili 

all’ottenimento di una buona convergenza dell’algoritmo stesso, 

è stato di 107. 

Ottenuto il design “ottimo”, sfruttando le potenzialità di postprocessing 

e statistica presenti all’interno di modeFRONTIER è 

stato possibile estrarre dalla considerevole 

mole di dati resisi disponibili, delle informazioni 

importanti per capire il legame tra le 

varibili di input e tra ciascuna di esse e 

l’obiettivo. Più in generale, questa fase consente 

di raccogliere delle preziose informazioni 

per capire più approfonditamente il 

problema studiato, soprattutto laddove esso 

dipenda da diversi input e output anche fra 

loro in forte contrasto, tipicamente con 

comportamento non lineare. 

Strumenti quali matrici di correlazione, grafici 

a coordinate parallele, filtri, bubble multidimensionali, 

hanno consentito di stabilire 

quale sia il peso dei singoli ingressi sui risultati, 

e quale sia il migliore intervallo di 

utilizzo tra i valori ammissibili, per ogni variabile. 

Infine, conoscendo come ogni variabile influisce sul risultato finale, 

partendo dal miglior design sono state implementate modifiche 

aggiuntive, che, integrando quelle previste dallo spazio 

parametrico sopra illustrato, hanno portato ad un ulteriore miglioramento 

delle performance del forno. 

Dall’attività nel suo complesso, ne è scaturita una profonda conoscenza 

del fenomeno fisico, delle relazioni fra le variabili, con 

soddisfacenti riscontri sperimentali. 

Risultati e Conclusioni 

Grazie alle simulazioni numeriche e all’esperienza dei tecnici 

Indesit nell’indirizzare decisioni e assunzioni da prendere, le 

prestazioni del forno da cucina sono migliorate nell’ordine del 

20% sullo scarto quadratico medio rispetto alla configurazione 

iniziale. 

Le prove sperimentali sul miglior design hanno confermato i risultati 

numerici ed un risparmio energetico dovuto ad una portata 

smaltita dalla ventola superiore a quella del design di partenza. 

Fig. 3 

Gianluca Mattogno, Indesit - gianluca.mattogno@indesit.com 

Fabio Damiani, EnginSoft - info@enginsoft.it

The Gas Turbine ventilation system is designed to supply 

the necessary amount of air for cooling and to prevent the 

accumulation of hazardous gases in the enclosure by 

maintaining a slight over-pressure. The classical GE 

approach to studying ventilation system operating 

conditions consists of modeling the whole system as a 

series of discrete losses, where the ASHRAE duct-fitting 

database provides the corresponding pressure loss 

coefficients. The system is solved by means of a onedimensional 

flow simulation tool (Flowmaster). 

The goal of this work was to improve the critical points 

that affect the above-mentioned procedure, such as 

modeling of complex fittings and bend interactions. For 

this purpose, dedicated CFD analyses were performed to 

characterize the loss coefficient for splitting and bend 

interactions at different operating conditions (split 

percentage and inlet flow rate) for two different 

ventilation systems. The resulting loss coefficient curves 

have been implemented within the corresponding onedimensional 

Flowmaster models. Finally, to characterize 

off-design conditions, a variable Heat Rejection model 

(obtained from previous CFD analyses) and real fan curves 

were used. 

This new approach produces more accurate results, as 

confirmed by the close agreement with experimental 

measurements. Among the benefits of using this new 

approach is the ability to characterize the flow behavior 

of complex fittings. This would be useful in the event of 

a fitting redesign or for noise reduction analyses. 

Current GE approach to studying 

Gas Turbine Ventilation Systems 

A ventilation system must provide a continuous source of 

cooling air over the entire Gas Turbine operation range in 

order to: 

maintain a uniform and constant airflow through the 

flange-to-flange Gas Turbine at all ambient conditions; 

remove heat and maintain the air temperature in the 

compartment below the operating limit. (The 

operating limit is set according to the temperature 

rating of the components located in the 

compartment); 

eliminate stagnation zones and prevent the 

accumulation of hazardous gases; 

prevent the ingress of dust and sand in gas turbines 

located in regions prone to sandstorm conditions by 

means of proper compartment pressurization. 


Combined 1D & 3D CFD Approach for 

GT Ventilation System analysis 

Specific Design Practices provide a general description, 

acceptance limits and design criteria that a ventilation 

system must meet for Oil & Gas applications (e.g., 

enclosure design temperature ranges, design pressure 

ranges, purging ranges, etc.). 

As mentioned, the current GE approach to studying GT 

ventilation systems consists of modeling the whole system 

as a series of “blocks”. Each block represents a source of 

pressure loss (concentrated loss) due to changes in shape 

(e.g., elbow, transition, etc.), flow direction or the 

presence of physical obstacles within the system. The 

ASHRAE duct-fitting database provides the corresponding 

pressure loss coefficients. 

Following the net balancing by means of a onedimensional 

flow tool (Flowmaster), the system is 

characterized in terms of velocities, pressures, and flow 

rate split. 

Critical points for this approach are the modeling of 

complex fittings and bend interactions. In order to 

improve the current Ventilation System calculation 

procedure, dedicated CFD analyses were performed for 

these critical points. A combined 1D & 3D CFD approach 

was adopted to study two different GE Ventilation 

Systems, called for simplicity System A and System B. 

Numerical calculations for System A 

The current System A Flowmaster network, modeled as a 

series of discrete losses, is shown in Figure 1. The 

Fig. 1 - System A Flowmaster model based on discrete losses.


enclosure is modeled as two heaters and the fan as two 

flow sources with a flow rate of 65000 m 3 

/h, estimated by 

using the enthalpy balance equation: 

were: 

= mass air flow [Kg/s], 

= enclosure heat rejection [W] 

= specifiwec heat at constant pressure [J/Kg °C] 

= maximum allowable outlet air temperature [°C] 

= max ambient temperature [°C] 

In order to develop a more suitable model (taking into 

account interactions, 3D characteristics of the fluid, etc.), 

dedicated ANSYS FLUENT CFD analyses were performed. In 

particular, a critical point for the discrete losses modeling 

is the flow split into the Load Compartment and the Gas 

Turbine Compartment (see Figure 2). 

Fig. 2 - Analyzed split (left) and Load Compartment final section (right), 

System A 

It is useful to define the coefficients K12 and K13 as: 

; 

where: 

P01 = inlet total pressure 

P02 = GT Compartment total pressure 

P03 = Load Compartment total pressure 

V2 = GT Compartment mean velocity 

V3 = Load Compartment mean velocity 

For the characterization of the flow 

split at different operating points, 

two test campaigns were performed. 

In both cases the inlet flow rate was 

fixed (65000 m 3 

/h and 130000 m 3 

/h, 

respectively) and, for each of these, a 

variable split percentage between the 

GT and Load compartments was used. 

These analyses provided K12 and K13, 

K12 (GT Compartment) K13 Load Compartment 

ASHRAE Database 0.4 0.71 

modified ASHRAE model 

(experience based) 

1.15 1.5 

CFD 0.4-0.6 2.28 

Table 1: Loss coefficients used for standard calculations, System A. 

defined in (2), as a function of the flow rate split (see 

Figure 3). 

Subsequently, these coefficients were implemented within 

the corresponding one-dimensional Flowmaster model. 

A comparison between the loss coefficients obtained 

using CFD and those coefficients used for standard 

calculations is summarized in Table 1. 

Finally, to better simulate the ventilation system a bend 

interaction analysis was performed on the Load 

Compartment final section, which is highlighted in Figure 

2 (for System B the geometry of this section is the same). 

The total loss coefficient as a function of the inlet 

velocity is shown in Figure 4. The loss coefficient 

decreases as the inlet flow velocity increases, and a good 

agreement with the ASHRAE database value was found for 

a velocity of about 5m/s. For higher velocity values the 

difference between the two curves (CFD and ASHRAE) 

starts to be significant. Again, the loss coefficient curve 

obtained was implemented within the new model. 

The fan, previously modeled as two flow sources, was 

replaced by the “FAN” element with the corresponding real 

operating curve. 

The final System A Flowmaster model including the main 

differences from the standard approach is shown in Figure 

5. 

The results obtained with the new model were compared 

with the results obtained by the ADV (Air Ducts and 

Ventilation) department using a model based on the 

ASHRAE loss coefficient with appropriate corrections 

based on experience and with the results obtained with a 

pure ASHRAE model (see Table 2). The reliability of each 

approach was evaluated through comparison with 

experimental data. 

Fig. 3 - K12 and K13 as a function of flow rate split for two different inlet flow rates, System A.

Fig. 4 - Loss coefficient curve for Bend Interaction. 

Fig. 5 - New System A Flowmaster model. 

Table 2 summarizes the results obtained for the enclosure 

pressure. Using the new approach we got a favorable level 

of approximation with respect to the measured value 

(error equal to 7%). The other two approaches yielded 

errors higher than 25%. 

Figure 6 shows for each model the load compartment 

velocity and the corresponding error from the measured 

value at clean filter house conditions. The measured mean 

velocity is 12.47 m/s. 

Both the new model and the modified ASHRAE model 

(experience-based) led to a high level of agreement (error 

Discrete loss model 

(ASHRAE) 

Discrete loss model 

(Experience based) 

New model 

(Flowmaster+CFD) 

Enclosure 

Pressure[mmH2O] 

Measured 

value[mmH2O] 

Error[%] 

54.40 43.0 26.5 

54.86 43.0 27.6 

40.00 43.0 -7.0 

Table 2: Enclosure pressure, clean filter house conditions, System A. 


lower than 5%). On the contrary, the pure ASHRAE model 

produced an error of 18%. 

Numerical calculations for System B 

Also for the System B split, several tests were performed 

to determine the split loss coefficients for different 

operating conditions. Figure 7 shows K12 and K13 as a 

function of the split flow rate percentage between the GT 

and Load compartments for an inlet flow rate equal to 

70000 m 3 

/h (design flow rate). As one can see, both 

curves follow a linear trend. 

Similar to the System A model, the new 

System B Flowmaster model contains the 

loss coefficient curves obtained from CFD 

analyses (including the bend interaction 

curve) and the real fan operating curve. 

Finally, in order to better simulate the 

heat removal, the heat rejection was 

modeled as a function of the mass flow 

rate, in accordance with recent studies 

performed by the SYS-OPT (System 

Optimization) department, that is: 

where: 

HR = heat rejection 

HR0 = reference heat rejection 

= mass flow rate 

0 = reference mass flow rate 

n = reference exponent 

The final System B Flowmaster model is shown in Figure 8. 

Figure 9 shows the GT and Load Compartment velocity 

obtained with the STD model (previous calculations) and 

the new model for dirty and clean filter house conditions. 

Fig. 6 - Load compartment velocity, clean filter house conditions, System A.


In both cases, the load 

compartment velocity obtained 

with the new approach is 

significantly higher than the old 

value (+49%). In particular, for 

the new approach, we got a split 

of 89-11% compared to a value of 

92.7-7.3% obtained from 

previous calculations. 

Considering that the target flow 

rate is 90-10%, the new approach 

again provides more accurate 

results. 

No significant variations between 

the two approaches in terms of 

enclosure pressure and 

temperature were found. 

Fig. 7 - K12 and K12 as a function of flow rate split, System B. 

Conclusions 

In this work, a combined 1D and 

3D numerical approach was 

adopted to study two GE 

ventilation systems. This 

approach, compared to the 

current one-dimensional 

approach, improves the 

simulation of the actual operating 

conditions in terms of inlet flow 

rate, duct velocity and enclosure 

pressure, as confirmed by the 

close agreement with experimental 

measurements. 

Among the benefits of using this new approach is the 

ability to characterize the flow behavior of complex 

fittings. This would be useful to support the redesign of 

fittings or for noise reduction analyses. 

Fig. 8 - New System B Flowmaster model. 

About GE Oil & Gas 

GE Oil & Gas (www.ge.com/oilandgas) is a world leader 

in advanced technology equipment and services for all 

segments of the oil and gas industry, from drilling and 

production, LNG, pipelines and storage, to industrial 

power generation, refining and petrochemicals. We 

also provide pipeline integrity solutions, including 

inspection and data management, and design and 

manufacture wire-line and drilling measurement 

solutions for the oilfield services segment. As part of 

our 'Innovation Now' customer focus and commitment, 

GE Oil & Gas exploits technological innovation from 

other GE businesses, such as Aviation and Healthcare, 

to continuously improve oil and gas industry 

performance and productivity. GE Oil & Gas employs 

more than 12,000 people worldwide and operates in 

over 100 countries. 

References 

[1]Miller, D.S.: Internal Flow Systems; 2nd Edition, Miller 

Innovations, 2008 

[2]Idelchik I.E.: Handbook of hydraulic resistance, 3rd 

Edition, CRC Begell House, 1994 

[3]ASHRAE Duct Fitting Database, Version 2.5.0. ASHRAE 

L. Barbato, M. Blarasin, S. Rossin 

GE Oil & Gas, 

Via Felice Matteucci 2, Florence, Italy 

Figure 9 - GT and Load compartment velocity for STD and New approach, 

System B.

I still can remember the time when, looking at a 3D CAD 

model of an engine block, I would start thinking about the 

best way to translate it into a PREP7 procedure. I would 

come up with something to mesh, but the next time I 

would have to start from scratch again. In those times, 

beam representations in conjunction with SIFs were the 

best way to go with crankshafts. Other components 

required similar efforts. 

Things evolved in a continuous fashion, but a 

discontinuity came when ANSYS changed its face 

completely with Workbench. At first I thought that 

dealing with it would have been a dive into a bottomless 

ocean, just like the first time I met a CONTA174. But I’ve 

always been a fundamentalist when it comes to new CAE 

techniques, so I tried to move to WB as quickly as I could 

and to the maximum possible extent. 

And my way of working radically changed: geometry 

import and meshing issues sharply decreased, and past 


ANSYS WB and a review of the design 

metrics in Piaggio: the case of the 

motor shaft 

models could be used as templates for new, similar 

analyses. This last aspect evolved dramatically with the 

introduction of WB projects, where bunches of 

interconnected analyses form now real CAE procedures, 

laid down with nearly no effort. 

Such a case happened just a few weeks ago when I came 

up against a crankshaft simulation. 

I had to use WB both as stand-alone application and as 

part of a CAE chain, including MBS and durability 

analyses. 

The simulations I had to perform required both linear and 

nonlinear models, involving the simulation of neighboring 

components, in addition to the crankshaft proper. WB 

allowed me to quickly setup a baseline model: DM fixed a 

few CAD issues and Mechanical Automatic Contact 

detection feature greatly speeded up the assembly setup. 

The CAD interface can sense CAD simplified 

representations, allowing to perform partial CAD imports, 

really useful when dealing with big CAD models. 

Generating all the other models I needed from the 

baseline one was really easy at a project level, duplicating 

when a different topology was needed and linking when 

only different load systems or different analysis types 

were required. 

That way, I could assess both the frictional load transfer 

capability and the fatigue performance of the crankshaft 

assembly. 

For the former I used nonlinear models, exploiting the WB 

contact features, whose default settings are much more 

error-proof than navigating among all the keyoptions and


real constants of the good old CONTA family. I could check 

the functionality of friction couplings with both standard 

and custom postprocessing quantities. The latter are 

easily definable with the aid of another WB feature: the 

Worksheets. 

With them you have an overview of your modeling stages 

in a neat tabular form. You can check the properties of the 

bodies included in an assembly, of the contact interfaces, 

or the available postprocessing quantities, to name a few. 

From a table you can jump to the relevant model element 

with a simple click. So Worksheets prove to be a nice tool 

to check what you’ve done. 

The fatigue analyses required the computation of 

load/stress transfer functions and of a Craig-Bampton 

modal representation [1]. The latter was calculated by 

means of a simple combination of rigid Remote 

Displacement features and of a Commands object: no more 

messing around with CERIGs, since WB did the job totally 

in the background. 

The results of the activity were not only fatigue safety 

factors and stress distributions; besides them, and above 

all, a neat trace of what I did has been left both at a 

Project and System level, in the WB jargon. The next time 

a similar component will have to be simulated, it will be 

an update process, not a generation one. Opening the WB 

project, the modeling procedure will be easily 

recognizable. 

In the past the CAE techniques had a hard time trying to 

be simultaneously fast and accurate; that slowed their 

integration into the development process of complex 

systems. I think that WB has been one of the major 

milestones in overcoming these problems, therefore 

allowing the simulations to be perceived as standard and 

required activities. 

For more information: 

Roberto Gonella - EnginSoft 


[1]R.R. Craig, M.C.C. Bampton - Coupling of substructures 

for dynamic analyses - AIAA Journal,vol. 6, n.7, 1968

EnginSoft 

interviews Ing. 

Paolo Nesti, 

Piaggio Group 

ABOUT THE PIAGGIO GROUP 

The Piaggio Group was founded in 1884 and nowadays 

is the European leader and one of the 

major players worldwide in the sector of twowheeler 

vehicles. The Group is also a global leader 

in the development and manufacture of 

commercial vehicles. The product range of the 

Piaggio Group includes scooters, motorbikes and 

motorcycles from 50 to 1,200 cc and the trademarks: 

Piaggio, Vespa, Gilera, Aprilia, Moto 

Guzzi, Derbi, Scarabeo. In the light commercial 

vehicle market, Piaggio is represented with its 

three- and four-wheeler vehicles and the trademarks: Ape, 

Porter and Quargo. 

The Group’s head office is based in Pontedera (in the province 

of Pisa, Italy). Since 2003, it is managed by the industrial 

holding Immsi S.p.A. Roberto Colaninno is the President and 

Managing Director of the Piaggio Group. 

The manufacturing plants are located in: Pontedera (Pisa), 

Noale and Scorzè (Venice), Mandello del Lario (Lecco), 

Martorelles (Barcelona, Spain), Baramati (India, in the 

Maharashtra nation), and Vinh Phuc (Vietnam). 

Furthermore, there is a joint venture in China (in Foshan, in 

the province of Guangdong). In 2009, the Piaggio Group sold 

607.700 vehicles worldwide: 410,300 two-wheeler vehicles 

and 197.400 commercial vehicles. Motorcycle racing is a very 

important business for the Piaggio Group. Some Piaggio 

models are well known for some world records: 95 world 

championships won in different fields and more than 500 victories 

in different competitions. Among the most successful 

brands is Aprilia. With its 45 global qualifications and 278 

wins in the GP, Aprilia is the most successful brand in the history 

of the GP Motorcycle Racing in Italy and Europe. 

The engineer Paolo Nesti graduated in Mechanical 

Engineering. Since 1988, the focus of his work has been on 

engines. Presently he is responsible for the design of engines 

and for computational systems at the Centro Tecnico 

Motori 2 Ruote of Pontedera. This Piaggio Group Center is 

dedicated to the development of two-wheeler vehicle power 

engines. 

1. What is or should be the role of innovation in the 

industrial and entrepreneurial world? 

Nowdays the market has enlarged itself and Global 

competition is our new work environment and we are not 


EnginSoft 

intervista l’ing. 

Paolo Nesti del 

Gruppo Piaggio 

IL GRUPPO PIAGGIO: PROFILO 

Il Gruppo Piaggio, fondato nel 1884, è il più 

grande costruttore europeo e uno dei principali 

player mondiali nel settore dei veicoli motorizzati 

a due ruote. È inoltre protagonista internazionale 

nel settore dei veicoli commerciali. 

La gamma di prodotti del Gruppo comprende 

scooter, moto e ciclomotori da 50 a 1.200cc 

con i marchi Piaggio, Vespa, Gilera, Aprilia, 

Moto Guzzi, Derbi, Scarabeo, e veicoli commerciali 

leggeri a tre e quattro ruote con le gamme 

Ape, Porter e Quargo. 

Il Gruppo ha sede a Pontedera (Pisa, Italia) e dal 2003 è 

controllato da Immsi S.p.A., holding industriale facente capo 

a Roberto Colaninno, che ricopre la carica di Presidente 

e Amministratore Delegato del Gruppo Piaggio. 

Sul piano della produzione, il Gruppo Piaggio opera nel 

mondo con una serie di stabilimenti situati a: Pontedera 

(Pisa), Noale e Scorzè (Venezia), Mandello del Lario 

(Lecco), Martorelles (Barcellona, Spagna); Baramati 

(India, nello stato del Maharashtra), Vinh Phuc (Vietnam). 

Il Gruppo Piaggio opera inoltre con una società in joint venture 

in Cina (a Foshan, nella provincia del Guangdong). Il 

Gruppo Piaggio nel 2009 ha complessivamente venduto nel 

mondo 607.700 veicoli di cui 410.300 nel business due ruote 

e 197.400 nel business dei veicoli commerciali. 

Di grande rilievo, per la produzione motociclistica del 

Gruppo Piaggio, sono le attività racing. Il Gruppo vanta infatti, 

nel proprio portafoglio di brand, marchi facenti parte 

a pieno titolo della storia del motociclismo sportivo mondiale, 

con un palmarès complessivo di 95 campionati mondiali 

conquistati nelle varie specialità e oltre 500 vittorie 

nelle varie specialità. Tra i marchi del Gruppo, Aprilia con 

45 titoli mondiali e 278 vittorie nei G.P. è il marchio italiano 

e europeo più vincente nella storia del Motomondiale. 

L'ing Paolo Nesti è laureato in ing. Meccanica, in Piaggio 

dal 1988, si è sempre occupato di motori. Oggi è il responsabile 

della Progettazione Motori e Sistemi di Calcolo 

del Centro Tecnico Motori 2 Ruote di Pontedera, dove si 

sviluppano tutti i motopropulsori per i veicoli a 2 ruote 

del Gruppo. 

1. Che spazio ha (e dovrebbe avere) l’innovazione nel 

mondo industriale/impresariale? 

La competizione globale a cui siamo chiamati non può es-


able to win our challenge playing on the simple grounds of 

cost reduction. A large group like ours, which operates on 

all major world markets, must be in a position of 

competitive advantage based on providing products more 

attractive to the customer, most original, high quality: we 

can achieve this goal looking at the expressed and latent 

Customers requirements even if they are also extremely 

different from themselves because economic, lifestyle and 

consumption reasons: in a word, we absolutely must 

innovate. Only in this way we can create products that 

ensure profitable business of the Company. Our group was 

very clear this need. 

Finally in order to achieve this goal, anything but simple, 

we must put attention to the market, to extensive technical 

expertise often interdisciplinary, to ability to express and 

synthesize new solutions, to fast implementation of ideas 

into products, to process control and to organization. 

2. What are the strategies for innovation and quality 

assessments pushing innovation? 

In order to innovate the right prerequisites are the 

following ones: 

to be very knowledgeable about both the product and 

customer; 

to have the expertise on technology and product 

knowledge of the best competitors; 

to be ensure that the work environment is "creative", 

giving space to persons who may contribute to the 

creation of ideas and having care to them encouraging 

their development aims by providing preferential 

channels for such projects. 

The MP3 version and Hybrid are strictly examples of this 

approach: the original idea was quickly passed to the 

development objective by providing our customers the best 

technology available on the market today (from a vehicle 

structure enhanced by a revolutionary hybrid engine based 

on a 4-stroke ie Euro3 with integrated management of the 

two engines, ride-by-wire, lithium batteries, plug-in 

without external power supply, electric reverse, etc.)… 

sere giocata sul semplice terreno della riduzione dei costi. 

Un grande Gruppo come il nostro, che opera su tutti principali 

mercati mondiali, deve porsi in una posizione di 

vantaggio rispetto alla Concorrenza basandosi sull'offerta 

di prodotti più attraenti per il Cliente, più originali, di 

elevata qualità globale, intendendo con questo termine 

anche e soprattutto la rispondenza ai bisogni espressi o 

latenti di Clienti anche estremamente diversi tra di loro 

per esigenze, contesti economici, stili di vita e di consumo: 

in una parola, deve fare innovazione. Solo in questo 

modo potrà realizzare prodotti profittevoli che garantiscano 

l'attività dell'Azienda. Il nostro Gruppo ha ben chiara 

questa necessità. 

Per raggiungere questo obiettivo, tutt'altro che semplice, 

occorrono attenzione al mercato, vaste competenze tecniche 

spesso interdisciplinari, capacità di esprimere e sintetizzare 

nuove soluzioni, velocità nell'implementazione 

delle idee in prodotti, controllo dei processi ed organizzazione. 

2. Quali sono le strategie per essere innovativi e quali 

valutazioni spingono all’innovazione? 

Per poter fare innovazione bisogna prima di tutto essere 

profondi conoscitori sia del prodotto sia della clientela, 

avendo le competenze specifiche sulle tecnologie e la conoscenza 

dei prodotti dei migliori concorrenti; occorre poi 

far sì che l’ambiente di lavoro sia “creativo”, dando spazio 

alle persone che possono contribuire alla nascita delle 

idee e favorendone lo sviluppo predisponendo canali 

preferenziali per tali progetti. 

L’MP3 prima e la versione Hybrid dopo sono un esempio di 

questo tipo di approccio: dall’idea originale si è passati in 

breve tempo allo sviluppo finalizzato, mettendo a disposizione 

dei clienti la miglior tecnologia disponibile ad oggi 

sul mercato (un veicolo dalla struttura rivoluzionaria 

impreziosito da un motore ibrido basato su un 4T i.e. Euro 

3, dotato di gestione integrata dei due propulsori, rideby-wire, 

batterie al litio, plug-in senza alimentatore esterno, 

retromarcia elettrica, etc.). 

3. Che ruolo ricoprono gli strumenti CAE e di prototipazione 

virtuale in tal senso? 

L'innovazione in campo industriale parte 

da un'idea che, attraverso stadi successivi 

di maturazione, si trasforma in 

uno o più prodotti che generano profitto 

per l'impresa. Questo processo non è 

spontaneo; passa bensì per una serie di 

verifiche ed eventuali correzioni, che 

determinano il tempo necessario per 

passare da idea a profitto. È quindi cruciale 

che queste attività siano veloci 

senza perdere accuratezza; gli strumenti 

CAE contribuiscono ad accelerare il 

processo, consentendo di ridurre i cicli 

di sperimentazione fisica che richiedono 

notevole dispendio di tempo e quin-

3. What role do the CAE and virtual 

prototyping tools in this regard? 

Innovation in industry starts with an idea 

that, through successive stages of 

maturation, changes in one or more 

products that generate profits for the 

company. This process is not spontaneous, 

but it is composed by multiple checks and 

corrections, which determines the time it 

takes to go from idea to profit. 

Therefore it is crucial that these activities 

are fast without losing accuracy and the 

CAE tools help speed up the process, 

reducing, for example, the cycles of 

physical experimentation saving 

considerable time and therefore money. 

In the early stages of development of an innovative idea 

the checks mentioned above are possible only at a 

conceptual level, the details are not yet available for 

physical prototypes At this level the simulation is the only 

way to correct any abuses of the process of maturation. 

4. How user needs have changed in recent years? 

User requirements have been prompted by a significant 

level of integration of CAE techniques and actually these 

technologies are completely integrated in the process 

design of product development. 

It obviously implies that the complexity of the simulation 

increase and, at the same time, the numerical results should 

be produced in more little time; and it seems to be a 

paradoxical situation. 

CAE Users therefore need intensive tools interfaced with 

those used in other business areas such as CAD software and 

processing of experimental data, another requirement is the 

ability to simulate not only the separate components, but 

also systems to study the interactions between 

components. This work field requires software tools fast, 

robust and accurate, and hardware with the maximum 

possible power from various points of view (processors, 

RAM, CPU, etc.)… All these things are strictly necessary 

without ever losing sight of the overall objective and 

having clear theoretical principles that govern the software 

used. 

5. What are the advantages pointed out in his 

professional experience and how has it changed its 

approach to the design/production? 

The advantages are actually well known to all actors 

(engineers, managers): first of all the savings in time and 

obviously money, having care to make virtual models (CAD, 

Multibody, FEM) analysis and CFD studies for engine 

performance, analysis of cooling, etc… 

The primary purpose of this type of activity, but not the 

only one, is to minimize the construction of physical 

prototypes, thus avoiding the long series of validation 

tests, limiting this activity to a stage "ripe" for the project. 

The construction of prototypes takes place when the phase 


di di denaro. Nelle fasi precoci di elaborazione dell'idea 

innovativa le verifiche prima citate sono inoltre possibili 

solo a livello concettuale, non essendo ancora disponibili 

i dettagli per realizzare prototipi fisici: a questo livello la 

simulazione rappresenta l'unico strumento per correggere 

eventuali derive del processo di maturazione. 

4. Come sono cambiate le esigenze degli utilizzatori negli 

ultimi anni? 

Le esigenze degli utenti sono state indotte dal sensibile 

livello d'integrazione che le tecniche CAE hanno ottenuto 

nel processo di sviluppo dei prodotti. Questa circostanza 

ha richiesto e continua a richiedere simulazioni sempre 

più complesse e, sebbene questo possa sembrare paradossale, 

sempre più veloci. Gli utenti CAE hanno perciò bisogno 

di strumenti fortemente interfacciati con quelli usati 

in altre aree aziendali, come i software CAD e di elaborazione 

dei dati sperimentali; un'altra esigenza è la possibilità 

di simulare non solo componenti isolati, ma anche sistemi, 

per studiare le interazioni fra componenti. Questo 

quadro richiede strumenti software veloci, robusti e accurati, 

e hardware con la massima potenza possibile sotto 

vari punti di vista (processori, memoria RAM, CPU, ecc.). 

Il tutto senza mai perdere di vista l’obiettivo globale ed 

avendo chiari i principi teorici che sovrintendono gli applicativi 

utilizzati. 

5. Quali vantaggi ha rilevato nella sua esperienza professionale 

e come è cambiato il suo approccio alla progettazione/produzione? 

I vantaggi sono oramai noti a tutti: prima di tutto il risparmio 

di tempo e quindi di denaro facendo modelli virtuali 

(CAD, Multidody, FEM), analisi e studi CFD per le prestazioni 

dei motori, analisi di raffreddamento, etc. 

Lo scopo primario di questo tipo di attività, ma non il solo, 

è ridurre al minimo la costruzione di prototipi fisici 

evitando così la lunga serie di prove di validazione, limitando 

questa attività ad una fase “matura” del progetto. 

La costruzione dei prototipi avviene quando la fase di calcolo 

(e di ottimizzazione) è stata completata, riducendo 

così la probabilità di dover apportare costose correzioni


calculation (and optimization) has been completed, thus 

reducing the likelihood of having to make costly 

adjustments to physical parts. 

The second key issue concerns the quality of the project: 

using CAE tools intelligently, we can quickly arrive at a 

definition of optimal projects, thus laying the foundation 

for a quality product. 

The third aspect is rewarding management and sharing of 

information: in this development process, it is essential to 

share information in real time and records management, to 

speed up internal processes of decision, correction, 

approval, etc… and then once again reduce the risk of 

introducing errors. 

6. What was the contribution of EnginSoft and how it has 

helped to enhance quality, capability and capacity of its 

industry/company? 

EnginSoft was invaluable in training, both basic and 

learning new software, even with the help of TCN. Basic 

education is key to preventing senseless and unconscious 

use of CAE tools: a real risk because of the simplicity and 

widespread use of software that claims to be based on 

complex theoretical apparatus. 

EnginSoft has also played and still plays a vital role in the 

innovation process, enabling access to technical and 

software embedded in a significant way with other 

development activities, and promoting effective networking 

among users, for example conferences and meetings with 

the resonance increasing year after year. 

7. What prospects he sees for the calculation codes in 

relation to the challenges of the future? 

It 'is now widely believed that competitive, if not survival, 

of different firms in the Old Continent and in the western 

world, in general, must be based on knowledge and 

innovation content of the products: the old standards of 

competitiveness are indeed falling down because the real 

big power of the new countries and these new ‘world’ will 

become strong players in the world economy. The 

instruments that promote the enrichment of science and 

technology products are becoming so indispensable to be 

su pezzi fisici. Il secondo aspetto fondamentale riguarda 

la qualità del progetto: utilizzando con intelligenza gli 

strumenti CAE, si può arrivare rapidamente ad una definizione 

ottimale dei progetti, ponendo così le basi per un 

prodotto di qualità. 

Il terzo aspetto premiante è la gestione e la condivisione 

delle informazioni: in questo processo di sviluppo è fondamentale 

la condivisione delle informazioni in tempo 

reale e la gestione della documentazione per velocizzare i 

processi interni di decisione, correzione, approvazione, 

etc. e quindi ridurre ancora una volta i rischi di introdurre 

errori. 

6. Qual è stato il contributo di EnginSoft e in che modo 

ha saputo valorizzare qualità, potenzialità e capacità 

della sua industria/impresa? 

EnginSoft è stata preziosa nel campo della formazione, sia 

a livello base che di apprendimento di nuovi software, anche 

con il contributo di TCN. La formazione di base è fondamentale 

per evitare usi scriteriati e inconsapevoli degli 

strumenti CAE: rischio concreto e diffuso a causa della 

semplicità d'uso di software che pure si basano su apparati 

teorici complessi. 

Enginsoft ha inoltre ricoperto e tuttora ricopre un ruolo 

basilare nell'ambito dell'innovazione di processo, rendendo 

possibile l'accesso a tecniche e software integrabili in 

modo significativo con le altre attività di sviluppo, e promuovendo 

con efficacia il networking fra utenti, per 

esempio con conferenze e incontri di risonanza crescente 

anno dopo anno. 

7. Che prospettive intravede per i codici di calcolo in relazione 

alle sfide poste dal futuro? 

È ormai convinzione diffusa che la competitività, se non 

la sopravvivenza, delle imprese del Vecchio Continente e 

del mondo occidentale in genere dovrà poggiare sulla conoscenza 

e sul contenuto innovativo dei prodotti; i vecchi 

canoni di competitività si stanno infatti sfaldando di 

fronte ai paesi che sono divenuti protagonisti dell'economia 

mondiale. Gli strumenti che favoriscono l'arricchimento 

scientifico e tecnologico dei prodotti stanno divenendo 

perciò indispensabili per riuscire a rimanere 

nel mercato; i codici di calcolo 

appartengono a tale categoria ed è perciò 

prevedibile che la loro evoluzione da 

prodotti di nicchia a strumenti di uso 

corrente si completi nei prossimi anni, 

accompagnata dall'accelerazione della 

loro potenza ed efficienza, resa possibile 

dall'analoga tendenza in campo hardware. 

8. Quali progetti, obiettivi e nuovi traguardi 

intende perseguire grazie all’uso 

di questi strumenti? 

Ritengo che nel medio termine non si verificheranno 

cambiamenti sostanziali nel-

able to stay in the market, the 

computer codes belong to this category 

and it is therefore like absolutely sure 

that their evolution from niche 

products to use current tools will be 

completed in the nextg years, and it 

will be accompanied by the 

acceleration of their power and 

efficiency, and it will be made possible 

from the analogous trend in the field 

hardware. 

8. What plans, objectives and new 

goals will be pursued through the use 

of these tools? 

I believe that in the medium term there 

will be no substantial changes in the use of CAE tools in 

Piaggio; during trouble-shooting in phase its usage has 

become a minority compared to the prediction, and it is 

evident that the products are developed today with growing 

contribution of the simulations. 

To predict the spread of multi-disciplinary optimization 

techniques will be Piaggio road map in this area, and the 

integration between CAE tools available today from various 

related subjects could push this design-way just as it 

happens between fluid dynamics and mechanics cold 

thematic. 

The number of experimental procedures for the qualification 

and a relative fee CAE activity will increase in the next 

future, so that physical tests will be conducted on 

prototypes already developed and optimized in terms of 

simulation, thereby increasing the likelihood of success of 

the test. 

In general, the use of CAE techniques will "dominate" more 

and more products in the sense that you can know more 

deeply the physical behavior, with clear benefits in terms of 

quality and reliability. 

9. And what we hope for the world of scientific 

technology to the continuing search for a dimension of 

creativity and competitiveness? 

The synthesis between creativity and competitiveness gets 

through the tools that technology provides. The task of 

scientific technology is just to make simple evaluations and 

complex activities that hinder the creativity of the people, 

thus removing the obstacles that typically prevent you from 

looking up to new ideas. It will be increasingly necessary to 

have integrated multidisciplinary tools, in order to further 

improve the aspects that are now viewed individually. 

Optimizers, being able to interact with different 

disciplinary tools, they have a high development potential 

that any company can develop applications specifically for 

doing their business. Also in this area Piaggio has already 

made some experiences and others are being set 

(modeFRONTIER and application specific simulation engine) 

to maximize engine performance and reduce development 

time for new products. 


l’uso degli strumenti CAE in Piaggio: l'uso in fase troubleshooting 

è divenuto minoritario rispetto a quello predittivo, 

indicando che i prodotti vengono sviluppati oggi con 

contributo crescente da parte delle simulazioni. 

Prevedo per Piaggio una diffusione delle tecniche di ottimizzazione 

multi-disciplinare, favorita dall'integrazione 

spinta oggi disponibile fra strumenti CAE afferenti da diverse 

materie, come ad esempio la fluidodinamica e la 

meccanica fredda. 

Aumenterà il numero di procedure di qualifica sperimentale 

con un corrispettivo CAE, in modo che i test fisici siano 

condotti su prototipi già studiati e ottimizzati a livello 

di simulazione, aumentando così la probabilità di successo 

della prova. 

In generale, l'uso delle tecniche CAE consentirà di "dominare" 

sempre più i prodotti, nel senso che sarà possibile 

conoscerne sempre più a fondo il comportamento fisico, 

con evidenti benefici in termini di qualità ed affidabilità. 

9. E cosa si auspica per il mondo della tecnologia scientifica 

alla continua ricerca di una dimensione tra creatività 

e competitività? 

La sintesi tra creatività e competitività passa proprio attraverso 

gli strumenti che la tecnologia mette a disposizione. 

Compito della tecnologia scientifica è appunto 

quello di rendere semplici le valutazioni e le attività complesse 

che ostacolano la creatività delle persone, rimuovendo 

così gli ostacoli che tipicamente impediscono di alzare 

lo sguardo verso nuove idee. Sarà sempre più necessario 

avere strumenti multidisciplinari integrati, per poter 

migliorare ancora gli aspetti che ora sono visti singolarmente. 

Gli ottimizzatori, potendo interagire con diversi strumenti 

multidisciplinari, hanno un elevato potenziale di sviluppo 

che ogni azienda può sviluppare facendo applicazioni 

ad hoc per le proprie attività. Anche in questo settore 

Piaggio ha fatto già alcune esperienze e altre sono in corso 

di impostazione (modeFRONTIER e applicativi specifici 

di simulazione motore) per massimizzare le prestazioni dei 

motori e per ridurre il tempo di sviluppo dei nuovi prodotti.


modeFRONTIER Used in the Design of 

Fatigue-Resistant Notches 

Fig. 1 - Researchers at Trinity College Dublin used modeFRONTIER 

in the Design of Fatigue-Resistant Notches 

Researchers at Trinity College Dublin in Ireland have used 

modeFRONTIER (mF) software to reduce stress 

concentration effects of notches and thus significantly 

improve the fatigue resistance of components. Many 

engineering components contain features such as notches 

and fillets, which are usually designed with a constant 

radius of curvature. 

However it has long been known that this is not the best 

solution. 

Variable-radius notches, in which the radius of curvature 

changes with position along the notch, can achieve much 

lower stress concentration factors with negligible change 

in the weight or size of the component. Nature has 

Fig. 2 - Finite element analysis of a variable-radius fillet in a bracket 

component. 

evolved variable-radius notches in trees, bones, etc; the 

German engineer Claus Mattheck showed that similar 

concepts could be applied to engineering structures. 

Professor David Taylor at Trinity College Dublin in Ireland 

wondered whether the variable-radius notch could be 

treated as an optimisation problem, and decided to use 

mF to solve it. Working with Matteo Toso and Professor 

Luca Susmel of the University of Ferrara in Italy, they 

considered the problem of a 90° fillet and used mF to seek 

for solutions within a design space consisting of different 

variable-radius fillets. They were able to find solutions 

better than those previously obtained using other 

methods, achieving reductions in the maximum stress of 

more than a factor of two. 

Experimental work conducted on steel samples showed 

that these predicted reductions translated exactly into 

real improvements in the fatigue strength of the 

components. Reductions in stress concentration factors 

can be highly beneficial, allowing designers to save 

weight, with consequent reductions in energy and material 

costs, without sacrificing reliability. The approach 

developed at Trinity College could be automated for use in 

industrial design, using mF in conjunction with FEM, to 

achieve real improvements in components of the future. 

Prof. David Taylor M.R.I.A., 

Mechanical Engineering, Trinity College Dublin 

For more information, please contact: 

Professor David Taylor, DTAYLOR@tcd.ie 

Department of Mechanical and 

Manufacturing Engineering 

The Department of Mechanical and Manufacturing 

Engineering undertakes research in a number of 

selected themes, including; Bioengineering, Fracture 

and Fatigue of Materials, Fluids, Acoustics and 

Vibration, Fluids and Heat Transfer, Manufacturing 

Technology and Systems, and Tribology and Surface 

Engineering. 

http://www.tcd.ie/mecheng/research/

A Multi-Objective Optimization with 

Open Source Software 

Sometimes it happens that a small-to-medium sized firm 

does not benefit from the advantages that could be 

achieved through the use of virtual simulation and 

optimization techniques. This represents in many cases a 

great limitation in the innovation of products and 

processes, and this can lead, in a very short time, to a 

complete exclusion from the market and to an inevitable 

end. 

Nowadays, it is mandatory to reduce as much as possible 

the time-to-market, while always improving the quality of 

products and satisfying the customer needs better that 

the competitors. In some fields it is a question of “life or 

death”. 

According to our opinion, the main reasons that limit or, 

in the worst case, make impossible the use of the virtual 

simulation and optimization techniques can be grouped 

into three categories: 

1. These techniques are not yet sufficiently known and 

the possible users do not have a great confidence in 

the results. Sometimes physical experimentation, 

guided by experience maturated through many years of 

practice, is thought to be the only possible way to 

proceed. This is actually wrong in the great majority of 

cases, especially when new problems have to be 

solved. A change of vision is the most difficult but 

essential step to take in this context. 

License 

Development 

Rough Phase Fine Phase 

Many possibilities are 

available 

Continuous improvement 

and a clear guideline 

Available features State of the art 

Technical support 

Usability 

Usually the distributor 

offers a technical support 

Easy-to-use and smart 

GUIs 

Customization Only in some cases 

GNU license largely used or similar 

versions with some restrictions 

Left to the community 

It strongly depends on “who” leads 

the development. Sometimes, very 

advanced 

features can be available. 

Usually no support is available but 

in some cases forums can help 

Some effort could be required to 

the user 

If the source code is available the 

possibility of customization and 

development is complete 

Table 1: The table compares some key features that characterize commercial 

and open source software, according to our opinion. 


2. Adequate hardware facilities considered necessary to 

perform an optimization are not available and 

therefore the design time becomes too long. We are 

convinced that, in many cases, common personal 

computers are enough to efficiently solve a large 

variety of engineering problems. So, this second point, 

which is often seen as an enormous obstacle, has to be 

considerably downsized. 

3. The simulation software licenses are much too 

expensive given the firm’s financial resources. Even 

though the large majority of commercial software 

houses offer a low-cost first entry license, it is not 

always immediately evident that these technologies 

are not just an expense, but rather a good investment. 

As briefly stated above, the second point often does not 

represent a real problem; the most important obstacle is 

summarized in the first point. People actually find it hard 

to leave a well-established procedure, even if obsolete, for 

a new one which requires a change in the everyday way of 

working. The problem listed in the third point can be 

solved, when possible, by using open source (see [1]), 

free and also home-made software. It is possible to find, 

with an accurate search on internet, many simulation 

software systems which are freely distributed by the 

authors (under GNU license in many cases). Some of them 

also exhibit significant features that usually are thought 

to be exclusive to commercial software. 

As usual, when adopting a new technology, it is 

recommended to consider both the advantages and the 

disadvantages. We have compared in Table 1 some aspects 

that characterize the commercial and the open source 

codes which should be considered before adopting a new 

technology. 

Open source codes are usually developed and maintained 

by researchers; contributions are also provided by 

advanced users all over the world or by people who are 

supported by research projects or public institutions, such 

as universities or research centers. Unfortunately, this can 

lead to a discontinuous improvement, not driven by a 

clear guideline, but rather left to the free contributions 

given by the community. On the contrary, commercial 

software houses drive the development according to wellknown 

roadmaps which generally reflect specific industry 

trends and needs. 

Commercial software is usually “plug-and-play”: the user 

has just to install the package and start using it. On the 

contrary - but not always - open source software could


require some skill and effort in 

compiling the code or adapting a 

package to a specific system 

configuration. 

Software houses usually offer to the 

customer technical support, which 

can be, in some cases, really helpful 

to make the investment profitable. An 

internet forum, when it exists, is the 

only way to have support for a user of 

an open source code. 

Another important issue is the 

usability of the simulation software, 

which is mainly given by a userfriendly 

graphical interface (often 

referred to as GUI). The commercial 

software usually has sophisticated 

graphical tools that allow the user to 

easily manage and explore large 

models in an easy and smart way; the 

open source codes rarely offer a similar suite of tools, but 

they have simpler and less easy-to-use graphical 

interfaces. 

The different magnitude of the investment can explain all 

these differences between the commercial and open 

source codes. 

However, there are some issues that can make the use of 

a free software absolutely profitable, even in an industrial 

context. Firstly, no license is needed to run simulations: 

in other words, no money is needed to access the virtual 

simulation world. Secondly, the use of open source 

software allows to break all the undesired links with third 

party software houses and their destiny. Third, the number 

of simultaneous runs that can be launched is not limited, 

and this could be extremely important when performing 

an optimization process. Last, but not least, if the source 

code is available all sorts of customizations are in 

principle possible. 

The results of a structural optimization, performed using 

Plate thickness [mm] Plate max dimensions [m] Available steel codes 

20 

30 

Vertical

Fig. 3 - A possible version of the C-shaped plate meshed in Gmsh. 

Fig. 4 - The CalculiX GraphiX window, where the von Mises stress for the Cshaped 

plate is plotted. 

not of practical interest, if it requires a steel characterized 

by a yield limit greater that 600 [MPa]. 

For this reason all the requirements collected in Tables 2 

and 3 have been included in order to drive the 

optimization algorithm to feasible and interesting 

solutions. 

Moreover, it is required that the hollow in the plate (H2max(R1,R2) 

x V2, see Figure 2) is at least 500 x 500 [mm] 

to allow the positioning of the transversal plates and the 

hydraulic cylinder. 

Another technical requisite is that the maximum vertical 

displacement is less than 5 [mm] to avoid excessive 

misalignments between the cylinder axis and the structure 

under the usual operating conditions. This limit has been 

chosen arbitrarily, in the attempt to exclude the designs 

that are not sufficiently stiff, taking into account, 

however, that the C-plate is a part of a more complex real 

structure which will be much more stiff than what is 

calculated with this simple model. 

A designer should recognize that the solution of such a 

problem is not exactly trivial. Firstly, it is not easy to find 

a configuration which is able to satisfy all the requisites 

listed above; secondly, it is rather challenging to obtain a 

design that minimizes both the volume of the plate (the 

weight) and the production cost. 


The open source software for the 

structural analysis 

Gmsh has been used as a preprocessor to manage 

the parametric geometry of the C-shaped plate and 

mesh it in batch mode. Gmsh has the ability to mesh 

a non-regular geometry using triangular elements; 

many controls are available to efficiently define the 

typical element dimension, the refinement depth 

and more. It is a very powerful software tool which 

is also able to manage complicated threedimensional 

geometries and efficiently mesh them 

using a rich element library. 

The mesh can be exported in a formatted text file 

where the nodes and the element connectivities are 

listed together with some useful information related 

to the so-called physical entities, previously defined 

by the user; this information can be used to correctly 

apply, for example, boundary conditions, domain 

properties and loads to a future finite element model. 

The CalculiX finite element software has been used to 

solve the linear elastic problem. Also in this case a batch 

run is available; among the many interesting features that 

this software offers are the easy input text format, and the 

ability to perform both linear and non-linear static and 

dynamic analyses. 

CalculiX also offers a pre and post processing 

environment, called CalculiX GraphiX, which can be used 

to prepare quite complicated models and, above all, 

display results. 

These two software tools are both well documented and 

also some useful examples are provided for new users. The 

input and output formats are, in both cases, easy to 

understand and manage. 

In order to make the use of these tools completely 

automatic, it is necessary to write a procedure that 

translates the mesh output file produced by Gmsh into an 

input file readable by CalculiX. This translation is a 

relatively simple operation and it can be performed 

without a great effort using a variety of programming 

languages; a text file has to be read, some information 

has to be captured and then rewritten into a text file 

using some simple rules. For this, a simple executable file 

(named translate.exe) has been compiled and it will be 

launched whenever necessary. 

A similar operation has also to be performed in an 

optimization context to extract the interesting quantities 

from the CalculiX result file and rewrite them into a 

compact and accessible text file. 

As before, an executable file (named read.exe) has been 

produced to read the .dat results file and write the 

volume, the maximum vertical displacement and the nodal 

von Mises stress corresponding to a given design into a 

file named results.out. 

Many other open source software codes are available, both 

for the model setup and for its solution. Also for the 

results visualization, there are many free tools with 

powerful features. For this reason the interested reader


can imagine the use of other 

tools to solve this problem in an 

efficient way. 

The optimization process driven 

by Scilab 

The genetic algorithm toolbox, by 

Yan Collette, is available in the 

standard installation of Scilab 

and it can be used to solve the 

multi-objective optimization 

problem described above. This 

toolbox is composed of some 

Design 

name 

H1 

[mm] 

H2 

[mm] 

V1 

[mm] 

V2 

[mm] 

Variable 

Lower bound 

[mm] 

routines which implement both a MOGA and a NSGA2 

algorithm and also a version for the operations that have 

been performed when running a genetic algorithm, that is 

the encoding, the crossover, the mutation and the 

selection. 

Upper bound 

[mm] 

Step 

[mm] 

H1 250 150. 5 

H2 500 1500 5 

V1 250 1500 5 

V2 500 1500 5 

V3 250 1500 5 

R1 50 225 5 

R2 50 225 5 

TH 20 50 10 

Table 4: The lower and upper bounds together with the 

step for the input variables. 

V3 

[mm] 

R1 

[mm] 

R2 

[mm] 

TH 

[mm] 

Cost 

[$] 

max vM 

stress 

[MPa] 

max vertical 

displacement 

[mm] 

These routines are extremely flexible 

and they can be modified by the user 

according to his or her own needs, 

since the source code is available. This 

is exactly what we have done, 

modifying the optim_moga.sci script to 

handle the constraints (with a penalty 

approach) and manage the infeasible 

designs efficiently (i.e.: all the 

configurations which cannot be 

computed); we have then redefined the 

coding_ga_binary.sci to allow the 

discretization of the input variables as 

Volume 

[mm 3 

] 

A 670 665 575 500 490 165 110 20 1304 577.7 4.93 3.53 10 7 

B 1155 695 725 545 840 185 165 30 1097 199.8 1.73 1.06 10 8 

Table 5: The optimal solutions. 

Fig. 5 - The Cost of the computed configurations can be plotted versus the Volume. Red points stand for the feasible 

configurations while the blue plus indicates the configurations that do not respect one constraint at least. 

The two green squares are the Pareto (optimal) solutions (A and B in Table 5). 

Fig. 6 - The vertical displacement for the design A. Fig.7 - The von Mises stress for the design A. 

listed in Table 4. Other 

small changes have been 

made to the routines to 

perform some marginal 

operations, such as writing 

partial results to a file. 

When the genetic algorithm 

requires the evaluation of a 

given configuration, we run a Scilab script which is 

charged to prepare all the text files needed to perform the 

run and then launch the software (Gmsh, CalculiX and the 

other executables) through a call to the system in the 

right order. The script finally loads the results needed by 

the optimization. 

It is important to highlight 

that this script can be easily 

changed to launch other 

software tools or perform other 

operations whenever necessary. 

In our case, eight input 

variables are sufficient to 

completely parameterize the 

geometry of the plate (see 

Figure 2): the lower and upper 

bounds together with the steps 

are collected in Table 4. Note 

that the lower bound of 

variable V2 has been set to 500 

[mm], in order to satisfy the 

constraint on the minimal 

vertical dimension required for 

the hollow. 

We can use a rich initial 

population, (200 designs 

randomly generated), 

considering the fact that a high 

number of them will violate the 

imposed constraints, or worse, 

be unfeasible. The following 

generations will however 

consist of only 50 designs, to 

limit the optimization time.

After 50 generations we obtain 

the results plotted in Figure 5 

and Table 5, where the two 

Pareto (optimal) solutions are 

collected. We finally decided to 

choose, between the two 

optimal ones, the configuration 

with the lowest volume (named 

as “A” in Table 5). 

In Figures 6 and 7 the vertical 

displacement and the von Mises 

stress are plotted for the 

optimal configuration named 

“A” (see Table 5). Note that 

during the optimization, the maximum value of the von 

Mises stress computed in the finite element Gauss points 

has been used, while in Figure 7 the von Mises stress 

extrapolated by CalculiX to the mesh nodes is plotted; this 

is the reason why the maximum values are different. 

However, they are both less than the yield limit 

corresponding to the steel type C, as reported in Table 3. 

Horizontal and vertical 

length of cuts starting 

from corners [mm] 

Cost 

[$] 

Fig. 8 - The vertical displacement for the modified 

design. 

max vM 

stress 

[MPa] 

max Vertical 

displacement 

[mm] 

Volume 

[mm 3 

] 

H1/3 1304 581.3 4.80 3.33 10 7 

Table 6: The modified design. It can be seen that there is an interesting 

reduction in the volume with respect to the original design, the “A” configuration 

in Table 5. Other output quantities do not present significant variations. 

Another interesting consideration is that the Pareto front 

in our case consists of just two designs: this shows that 

the solution of this optimization problem is far from 

trivial. 

The design of the C-shaped plate can be further improved. 

If we run other generations with the optimization 

algorithm better solutions could probably be found, but 

we feel that the improvements that might be obtained in 

this way do not justify additional computations. 

Substantial improvements can be achieved in another way. 

Actually, if we look at the von Mises stress distribution 

drawn in Figure 7 we note that the corners of the plate do 

not have a very high stress level and that they should not 

influence the structural behavior very much. 

A new design can be tested, cutting the corners of the 

plate; for the sake of simplicity we decided to use four 

equal cuts of horizontal and vertical dimensions equal to 

H1/3, starting from the corners. The results are drawn in 

Figures 8 and 9, which can be compared with Figures 6 

and 7. 

As expected, there is a reduction in volume with respect 

to the original design, but no significant variations are 

registered in the other outputs. This corroborates the idea 

that the cut dimensions can be excluded from the set of 


Fig. 9 - The von Mises stress for the modified design. 

input variables, since the output does not strongly 

dependent on them, and this leads to a simpler 

optimization problem. 

The cost does not change; actually it represents the cost 

of the rectangular plate needed to produce the C-shaped 

profile. 

Other configurations with a lower volume can be probably 

found with some other runs; however, the reader has to 

consider that these improvements are not really 

significant in an industrial context, where, probably, it is 

much more important to find optimal solutions in a very 

short time. 

Conclusions 

In this work it has been shown how it is possible to use 

open source software to solve a non-trivial structural 

optimization problem. 

Some aspects which characterize the commercial and the 

open source software have been stressed in order to help 

the reader to make his or her own best choice. We are 

convinced that there is not a single right solution but 

rather that the best solution has to be found for each 

situation. 

Whichever the choice, the hope is that virtual simulation 

and optimization techniques are used to innovate. 

References 

[1]Visit http://www.opensource.org/ to have more 

information on open source software 

[2]Scilab can be freely downloaded from: 

http://www.scilab.org/ 

[3]Gmsh can be freely downloaded from: 

http://www.geuz.org/gmsh/ 

[4]Calculix can be freely downloaded from: 

http://www.calculix.de/ 

Contacts 

For more information on this document please contact the 

author: 

Massimiliano Margonari - Enginsoft S.p.A. 

info@enginsoft.it


ANSYS 13: Il punto sui solutori per 

modelli di grandi dimensioni nelle 

simulazioni meccaniche 

Una delle grandi sfide per i software di simulazione basati sul 

metodo degli elementi finiti è l’efficienza nel trattare modelli 

di grandi dimensioni, e quindi: 

di conservare i dettagli presenti nei modelli CAD; 

di avere risultati accurati; 

di disporre di algoritmi veloci e robusti per la meshatura; 

di disporre di CPU sia su desktop che su cluster. 

In particolare è sempre più vero che il costo della CPU è inferiore 

al costo uomo, e cioè che, in presenza di algoritmi affidabili 

per la meshatura di modelli di grandi dimensioni, 

conviene ricorrere a questi piuttosto che svolgere un pesante 

lavoro di “defeaturing” e di meshatura semiautomatica. 

All’aspetto dei costi si affianca - ed è più importante di questo 

- l’aspetto dell’affidabilità dei risultati. Esso dipende in 

larga parte dalla qualità del modello e quindi anche della 

mesh. 

La release 13 del codice ANSYS Mechanical offre una risposta 

molto convincente a questi problemi, trattati in ottica industriale. 

Infatti: 

orienta all’High Performance Computing consentendo 

l’uso di CPU diverse; 

produce modelli schematizzati automaticamente in 

maniera adeguata; 

contiene acceleratori specifici. 

La novità della release R13 di ANSYS, infatti sta nel saper 

adattare il nuovo paradigma di processamento del calcolo per 

via numerica nel campo delle applicazioni meccaniche. Nel 

passato infatti l’elaborazione numerica è sempre stata assegnata 

alle potenzialità della CPU in una architettura in cui la 

CPU svolgeva un ruolo centralizzato. Oggi, invece, i principali 

produttori di schede grafiche hanno messo a punto nuove 

architetture che permettono incrementi consistenti delle prestazioni 

grafiche: la novità sta nella capacità di sfruttare il 

gran numero di ALU (Arithmetic Logic Unit) per eseguire algoritmi 

in parallelo. Seguendo la comparsa nel tempo di queste 

soluzioni si può far riferimento alle schede progettate da 

nVIDIA con tecnologia CUDA e dalla ATI con tecnologia 

STREAM. 

ANSYS a sua volta ha sviluppato algoritmi paralleli che eseguono 

calcoli in doppia precisione sfruttando le risorse relative 

alla tecnologia “GPU based”. 

In generale e per comprendere l’attenzione dedicata da 

ANSYS al problema di accelerare l’analisi si può far riferimento 

anche alle tecnologie disponibili nelle precedenti versioni. 

Queste tecnologie, del resto, sono state rese ancora più 

efficaci nell’ambito degli aggiornamenti sviluppati da ANSYS. 

Si richiamano: 

SMP (Shared memory Processing), tecnologia consistente in 

un’architettura di più CPU che condividono la stessa memoria. 

DMP (Distributed Memory), tecnologia consistente in un’architettura 

di più CPU con una porzione di memoria dedicata 

a ciascuna CPU. 

Se si confrontano queste due architetture solamente sulla base 

del tempo di calcolo risulta più efficiente la tecnologia 

DMP, sua volta più costosa dal punto di vista dell’hardware. 

Il vantaggio della tecnologia DMP rispetto al SMP risulta però 

inferiore se il tempo di calcolo è misurato come intero 

“elapsed time”, intendendo con questo il tempo relativo all’intero 

processo incluse le fasi precedenti e successive all’analisi 

vera e propria. 

Se quindi si ragiona in base al “dpd” (design produced per 

day) il rapporto di efficienza DMP/SMP è meno significativo. 

Più precisamente questo confronto è valido fino ad 8 CPU 

perché al di sopra di questo numero la SMP satura ed la DMP 

diventa oltremodo vantaggiosa anche in termini di “dpd”. 

Altra tecnologia del software ANSYS per ridurre i tempi di 

esecuzioni delle analisi è la VT (Variational Technology) che 

implementa gli algoritmi di Taylor, Pade’ e Roms.

Essa non può però essere utilizzata in tutti i casi. Si applica 

ad analisi termiche, ad analisi in frequenza e anche ad alcuni 

problemi di non linearità strutturali quali le tematiche di 

creep. Non possono, però, essere presenti nel modello elementi 

CONTACT e questo, per certi versi, è il limite di tale approccio. 

Infine è utile ricordare quanto certe metodologie di analisi di 

ben solida ed antica applicazione siano utili e rinnovabili 

nelle situazioni più complesse e quanto queste stesse tecniche 

possano beneficiare comunque delle generali accelerazioni 

legate all’aumento delle capacità di calcolo (esempio “GPU 

technique”). 

Ricordandole brevemente esse sono: 

Submodelling: valutazione locale del gradiente delle tensioni 

in situazioni alla De Saint Venant 

Substructuring: riduzione del modello a condensazioni di 

masse e rigidezze e ripetizione delle stesse per parti del 

modello ripetitive 


CMS: Component Mode Synthesis è una specie di sottostrutturazione 

con accoppiamento delle zone di interfaccia 

tramite equazioni di ‘coupling’ 

I tre sistemi sono stati rivisti nell’ottica dei miglioramenti 

progettati per la nuova release. 

Per concludere uno sguardo al futuro: le tecnologie per l’accelerazione 

della velocità di calcolo richiedono parallelamente 

il potenziamento dell’hardware. Gli sviluppi previsti in 

ANSYS tengono in conto l’evoluzione nell’hardware per migliorare 

le prestazioni. La release 13 rappresenta quindi un 

primo passo nell’adattamento delle metriche della tecnologia 

GPU, finalizzato a migliorare la scalabilità del software in 

tutte le applicazioni seguendo così quanto già fatto con successo 

ad esempio nelle applicazioni relative alla CFD. 

Per maggiori informazioni: 

Emiliano D’Alessandro - EnginSoft 


La simulazione di sistema in ANSYS: 

Simplorer 

La release 13 di ANSYS lega nello stesso ambiente le tecnologie 

per la simulazione elettromagnetica in bassa ed 

alta frequenza prodotte da Ansoft. Di queste, abbiamo dato 

informazione in edizioni precedenti della newsletter. 

Ci occupiamo qui invece di Simplorer, la tecnologia per la 

simulazione di sistema in ANSYS. 

Simplorer è un software di simulazione Multi-Domain che 

consente di modellare, simulare, analizzare e ottimizzare 

sistemi complessi, come sistemi elettromagnetici, elettromeccanici, 

elettrotermici, e più in generale, meccatronici 

e cibernetici. 

Se quindi da un lato ANSYS fornisce strumenti per la modellazione 

e la simulazione di singoli componenti in diverse 

fisiche e discipline, dall’altro, attraverso una tecnologia 

come Simplorer, essa consente la simulazione a livello di 

sistema. In altre parole vengono messe a disposizione metodologie 

e tecniche affinché singoli componenti possano 

essere analizzati simultaneamente in un unico modello, 

tenendo in considerazione le mutue interazioni tra di essi. 

Fig. 1 - Simplorer in interfaccia ANSYS WB. 

L’utilizzo delle caratteristiche di modellazione di Simplorer 

consente quindi ai progettisti di realizzare prototipi virtuali 

considerando tutti gli aspetti ed i componenti di un 

sistema, quali ad esempio i componenti elettronici, i sensori, 

gli attuatori, i motori elettrici ed i generatori, i propulsori 

ibridi, i convertitori di potenza così come i con- Fig. 2 - Modelli e linguaggi per la simulazione di sistema in Simplorer.


Fig. 3 - Link dinamico tra HFSS-Simplorer (a) e SIwave-Simplorer (b). 

trolli ed i software embedded. 

Una tale metodologia si realizza attraverso l’implementazione 

di schemi circuitali, modelli analitici ed a parametri 

concentrati, circuiti equivalenti, tecniche di cosimulazione, 

reti multi-livello ecc… 

Di seguito vengono analizzate alcune delle principali caratteristiche 

di Simplorer: 

1) Tecniche di modellazione 

Simplorer offre diverse tecniche di modellazione inclusi 

circuiti, diagrammi a blocchi, macchine a stati, equazioni 

e linguaggi di modellazione come il linguaggio VHDL-AMS, 

il SML (Simplorer Modeling Language) ed il C/C++. 

L’impiego simultaneo di tali strumenti consente di modellare 

sistemi caratterizzati da segnali analogici, digitali o 

analogico-digitale. Questo approccio elimina la necessità 

di effettuare trasformazioni matematiche, tipicamente 

soggette ad errori. 

Una tale flessibilità nelle tecniche di modellazione fa di 

Simplorer uno strumento molto efficace all’interno di un 

gruppo di lavoro poiché professionisti di diversa estrazione 

tecnica possono far confluire modelli realizzati con linguaggi 

diversi all’interno dell’unica piattaforma di simulazione 

di Simplorer. 

In Figura 2 una sintesi delle tecniche 

di modellazione a disposizione di 

Simplorer. 

2) Physics-based modeling 

Per modelli per i quali è richiesto un 

elevato livello di accuratezza, 

Simplorer fornisce un link diretto ad 

altri software ANSYS, tra questi: 

Maxwell, Q3D Extractor, RMxprt, 

PExprt, HFSS, SIwave, ANSYS Icepak, 

ANSYS Rigid Dynamics e ANSYS 

Mechanical. 

In particolare dall’ultima versione 

(Simplorer 9.0.1) è possibile integrare 

all’interno della simulazione di 

Simplorer i modelli agli elementi fini- 

ti realizzati in SIwave e HFSS. Tale procedura si basa sulla 

valutazione e caratterizzazione a parametri S di un modello 

FEM. 

Tipicamente, una volta definite opportunamente le porte 

di input ed output nei modelli agli elementi finiti, SIwave 

e HFSS consentono infatti di esportare la matrice di scattering 

verso Simplorer. 

Come mostrato in Figura 3 il link SIwave-Simplorer consente 

di effettuare in Simplorer analisi di Signal integrity 

su schede PCB, mentre l’integrazione di HFSS permette 

l’analisi elettromagnetica di sistema anche in alta frequenza. 

Le tecniche con le quali è possibile includere modelli realizzati 

con altri software di casa ANSYS sono in particolare 

due: La tecnica della cosimulazione e la tecnica della 

model order reduction (MOR). 

La cosimulazione o “Co-Simulation” (co-operative simulation) 

è una metodologia di simulazione che consente a 

componenti singoli di essere simulati in maniera simultanea 

da differenti software. Questa tecnologia permette 

quindi ai due software di scambiarsi informazioni, quali ad 

esempio boundary conditions o time steps, in maniera collaborativa 

e sincronizzata. 

Fig. 4 - Control Design per il modello multy-body di un braccio di un escavatore in ANSYS WB.

Fig. 5 - Analisi di sistema e circuit coupling in ANSYS. 

Questo tipo di tecnica è implementabile con i modelli di 

Maxwell e con i modelli Multy Body di ANSYS, per i quali 

Simplorer fornisce gli eventuali controlli (Fig 4). 

La tecnica della Model Order Reduction (MOR) è una disciplina 

della teoria dei sistemi e dei controlli che studia le 

proprietà dei sistemi dinamici in modo tale da ridurne la 

complessità, preservandone il comportamento agli ingressi 

e alle uscite (input ed output). 

Attraverso la tecnica della Model Order Reduction è possibile 

trasferire nell’ambiente di simulazione di Simplorer 

modelli a parametri concentrati estratti da modelli agli elementi 

finiti realizzati tra gli altri in ANSYS Mechanical, 

ANSYS Fluent e ANSYS ICEPack. 

In Figura 5 viene sintetizzato lo stato dell’arte dell’integrazione 

di sistema in ANSYS. 

3) Cosimulazione con tool esterni ad ANSYS 

Programmi in C/C++, MATLAB® / Simulink®, ModelSim®, 

QuestaSim® e Mathcad® possono essere integrati direttamente 

in Simplorer attraverso la tecnica della cosimulazione. 

Questo permette una semplice e rapida implementazione di 

modelli realizzati anche con software 

esterni al portafoglio dei 

prodotti ANSYS. 

La diretta integrazione dei modelli 

nel loro ambiente di simulazione 

evita la traduzione del modello, 

consente di risparmiare tempo 

per la progettazione, e permette 

la comunicazione e lo scambio di 

informazioni tra diversi progettisti. 

4) Analisi Statistiche e di 

Ottimizzazione. 

Optimetrics, tool embedded in 

Simplorer, consente di effettuare 

analisi parametriche, di ottimizzazione, di sensitività, 

e di tuning al fine di ottenere un progetto 

ottimizzato, in relazione a criteri di performance 

fissati, raggiungendo il miglior trade-off 

possibile. 

In questo senso Simplorer può usufruire di tutta 

la potenza di calcolo a disposizione perché in 

grado di effettuare analisi distribuite. 

In particolare, dall’ultima release, Optimetrics 

viene incluso all’acquisto nel pacchetto software. 

5) Tools di caratterizzazione di power devices. 

Simplorer supporta strumenti e sistemi per la caratterizzazione 

di dispositivi di potenza quali 

IGBT e converter AC/DC. 

Per quanto riguarda l’analisi degli IGBT, Simplorer 

fornisce due diversi metodi di caratterizzazione: dinamica 

e mediata (dynamic and average). La caratterizzazione dinamica 

consente una maggiore precisione nel descrivere i 

fenomeni di switching che avvengono in questi dispositivi, 

mentre la caratterizzazione average permette una modellazione 

del dispositivo tale da consentire tempi di simulazione 

più ridotti, fornendo comunque una stima delle 

perdite medie durante le fasi di switching. 

Entrambe le metodologie descritte leggono gli input necessari 

alla caratterizzazione del dispositivo direttamente 

dai datasheet messi a disposizione dai fornitori. 

Per quanto riguarda la definizione e la caratterizzazione di 

dispositivi elettronici inoltre è possibile accedere in rete 

(http://model.simplorer.com) ad una vasta libreria di modelli 

di componenti quali Diodi, MOFSFETs ed IGBTs. 

In Figura 6 viene illustrato un esempio di simulazione multi 

dominio in ANSYS Simplorer. 

Per maggiori informazioni 

Emiliano D’Alessandro - EnginSoft 


Fig. 6 - Simulazione elettro-termica di un commutatore IGBT in Simplorer. 

Newsletter EnginSoft Year 8 n°1 - 35


ICEPAK 13.0: buone notizie per i 

progettisti elettronici 

I malfunzionamenti legati al surriscaldamento, le eccessive 

sollecitazioni termiche sulla struttura e la gestione non ottimale 

della distribuzione dei flussi di calore sono problemi comuni 

nell’ambito della progettazione dei dispositivi elettronici. 

Tutti questi problemi possono essere studiati e risolti 

utilizzando ANSYS Icepak. 

Icepak è un codice di fluidodinamica computazionale robusto 

e completo creato specificatamente per il controllo termico 

dei dispositivi elettronici. Caratterizzato da un’interfaccia 

estremamente intuitiva, permette di eseguire analisi termo 

fluidodinamiche stazionarie e tempo transienti simulando 

tutte le modalità di trasferimento del calore (conduzione, 

convezione, radiazione e scambio termico coniugato). 

Presente sul mercato da ormai più di dieci anni, Icepak è 

giunto alla release 13.0 che ha aggiunto nuove funzionalità 

in grado di aumentarne la facilità di utilizzo e di renderlo ancora 

più attraente al progettista elettronico. 

Di seguito sono indicate le principali novità introdotte nella 

versione corrente suddivise per aree tematiche: 

Integrazione con ambiente Workbench 

Il graduale inserimento di Icepak all’interno dell’ambiente di 

lavoro Workbench, iniziato con la release 12.0, continua nella 

versione attuale. 

In relazione alla fase di pre-processamento geometrico, sono 

stati implementati in Design Modeler alcuni strumenti (riuniti 

nel gruppo dei tools denominato Electronics – vedi Figura 

1) che permettono di semplificare entità geometriche anche 

complesse e di convertirle direttamente in oggetti nativi di 

Icepak con un notevole risparmio di tempo da parte dell’utente. 

Fig. 1 - Strumenti di semplificazione e conversione geometrie in Design 

Modeler: Electronics tools 

Fig. 2 - Strumenti di semplificazione e conversione geometrie in Design 

Modeler: Electronics tools 

Per quanto riguarda la fase di post-processing, tutte le funzionalià 

del visualizzatore interno ad Icepak sono ora disponibili 

nel software unico di postprocessing di Workbench 

(CFD-Post) che è uno strumento in generale più potente e in 

grado di gestire griglie di calcolo anche di notevoli dimensioni 

(vedi Figura 2). 

Sempre nell’ambito di una progressiva integrazione di Icepak 

nell’ambiente di lavoro Workbench è da registrare il miglioramento 

dell’algoritmo di trasferimento del campo termico verso 

il solutore strutturale con una conseguente velocizzazione 

dei tempi di calcolo per le analisi FSI 1way (vedi Figura 3). 

Mesh 

Il generatore di griglie di calcolo interno ad Icepak è stato 

ulteriormente sviluppato al fine di automatizzare il processo 

di meshing. Tra le nuove features ricordiamo: 

La nuova opzione di meshing multi level (2D zero Cut 

Cell) permette un notevole risparmio di elementi di 

griglia nel caso di modelli 2.5D ed è molto utile per la 

modellazione delle tracce sulle printed circuit board (PCB) 

(vedi Figura 4); 

il metodo di estrusione di griglia (Extruded Mesh) è stato 

implementato al fine di controllare il numero di celle 

posizionate nello spessore delle PCBs e dei Packages; 

L’utilizzo degli O-Grid per la generazione della griglia di 

calcolo è stato ora esteso anche ad oggetti 2D 

(precedentemente era applicato solo a entità geometriche 

tridimensionali) con notevole beneficio sia della qualità 

della griglia che della risoluzione dei boundary layer 

termici; 

È ora possibile creare mesh non conformi per i singoli 

componenti di un Assembly mettandoli a diretto contatto 

tra di loro (Zero Slack Assembly). Questo miglioramento 

nella gestione delle interfacce tra mesh porta ad una 

notevole riduzione del numero degli elementi da usare per 

discretizzare oggetti quali Heat sink, BGA etc. dove i

Fig. 3 - Trasferimento diretto dei dati da Icepak ad ANSYS Meshanical 

Fig. 4 - Multi Level 2D Cut Cell Meshing 

singoli componenti hanno dimensioni caratteristiche 

molto differenti. 

Modellazione 

Uno dei punti di forza di Icepak è la possibilità di generare 

modelli fisici rappresentativi dei singoli componenti elettronici 

da includere nella simulazione numerica quando il focus 

dello studio non è a livello della componentistica (scala [m]) 

ma a livello di sistema (scala [m]) – vedi Figura 5. Questo 

punto di forza è stato ulteriormente sviluppato con: 

Il miglioramento del sistema di caratterizzazione dei 

packages elettronici denominato Delphi Extractor che 

permette di creare in modo automatico una 

rappresentazione RC (Resistiva Capacitiva) di packages 

quali BGA, Exposed Die BGA e QFP (Figura 6); 

L’introduzione di una nuova macro per assistere nella 

modellazione delle Heat Pipes (Figura 

7). 

La versione 13 è inoltre supportata per oggetti 

2D quali Fans, Grilles, Walls e 

Openings la geometria derivante da CAD. 

Ciò permette di utilizzare forme geometriche 

realistiche anziché approssimazioni a 

geometria poligonale per tali oggetti. 

Solutore 

Le novità fondamentali del solutore possono 

essere riassunte in 2 punti: 

Fig. 7 - Caratterizzazione dei componenti: Heat 

Pipes 


Sono stati introdotti due nuovi modelli di radiazione: il 

“surface to surface” e il “ray tracing”. Il primo modello è 

semplice ed economico, adatto alla maggior parte delle 

applicazioni con mezzi trasparenti. Il secondo modello è 

molto più generale ed accurato ma dai costi 

computazionali elevati. Da sottolineare inoltre il supporto 

per il carico termico solare (compatibile con tutti i 

modelli di radiazione) che permette di tenere in conto 

della radiazione proveniente dall’ambiante esterno e del 

suo orientamento nello spazio. 

Fig. 5 - Icepak permette di eseguire analisi termiche a varie scale dimensionali 

Fig. 6 - Caratterizzazione dei componenti: Delphi Extractor 

È ora possibile importare ed esportare oggetti networks 

cioè modelli bidimensionali che rappresentare circuiti 

integrati tramite file CSV/Excel. 

Per concludere questa visione di insieme della principali novità 

introdotte con Icepak 13 è da segnalare la possibilità di 

interagire in modo bidirezionale con 

SIwave, software per il calcolo dell’integrità 

di segnale. Icepak può ora dialogare 

con SIwave fornendo mappe di temperatura 

e ricevendo campi di potenza. 

Interazione che risulta fondamentale per 

analisi dove le proprietà elettriche del dispositivo 

dipendono dalla temperatura. 

Per esempi, materiale e richieste di informazioni: 

Ing. Matteo Nobili - EnginSoft 

info@enginsoft.it


Development of the Novel Opencell 

A completely new metal sandwich panel concept, 

Opencell, has been developed. Instead of the 

conventional three constituent panel structure 

(sheet/core/sheet), an integral cut-and-formed face sheet 

and the core, and a solid face sheet are used. This concept 

provides a reduction in the number of joining components 

and thus manufacturing phases can be decreased for a 

more cost-efficient process. An increased number of 

design variables means potential for tailored properties. 

Unlike many traditional metal sandwich panels, the 

structure can have equal mechanical properties in the 

longitudinal and transversal directions, and in specific 

applications this concept provides stiffer solutions than 

the conventional sandwich panels. 

TECHNOLOGY REVIEW 

Metal sandwich panels offer a number of outstanding 

properties allowing the designer to develop light and 

efficient structural configurations for a large variety of 

applications. The most established type of all-metal 

sandwich panels is the use of directional stiffeners 

Fig. 1 - The Opencell structure can take the form of flat, single side curved and doubly side curved shapes. 

between two solid face sheets, such as straight webs (Icore), 

(rectangular) hollow sections (O-core), hat or 

corrugated sheets (Vf/V-core), etc. [1]. 

In contrast to the previous ones, called calottes, formed 

indents to separate the panel face sheets provide more 

isotropic properties. However the closed nature of calottes 

limits their maximum depth (formability) [2, 3]. 

Another developing direction of 

all-metal sandwich panel 

technology is towards lattice 

truss core structures. This has 

evolved into a completely new 

approach for a metal panel 

structure concept called 

Opencell. This invention, 

described in patent application 

WO/2009/034226, includes a 

panel structure wherein the core 

structure is formed from face 

sheets that are mechanically 

connected to each other using connection members 

formed from one of the face sheets, i.e. without any 

addition of the core material [4]. 

CONCEPT DEVELOPMENT 

The motivation of the product development naturally is 

increased performance. In principle, two approaches exist 

to develop more efficient structures: either the 

application of new materials or the use of novel structural 

design – or a combination of these. Opencell idea itself 

introduces a large number of design variables for tailored 

properties. With Opencell one can build panels with 

balanced transversal and longitudinal stiffness properties 

and challenging panel shapes are possible (see Figure 1). 

Within certain limits, panel height can be increased 

without mass penalty. This means increased bending 

stiffness offering potential for weight savings. 

The initial concepts were not very efficient in terms of 

structural performance and the project team focused on 

the concept development. Different geometrical layouts 

(see Figure 2), providing a range of structural performance 

and various types of packing patterns, were developed, 

simulated using FEA, and evaluated for the proper 

understanding of their mechanical behavior. 

The purpose of the work was to make general comparative 

analyses using a design study, for which a reference 

application was adapted from an earlier sandwich project 

Fig. 2 - Some of the studied Opencell concepts. Three right-most solutions represent Opencell Delta concepts. 

All unit cells are in mutual scale, i.e. height of the panel is constant.

Table 1. Panel dimensions in the design studies. 

[5]. This consisted of a beam-like supported 54 mm high 

panel with a support span L of 2 m and a uniform pressure 

load of 4800 Pa. A deflection constraint was set to L/300. 

The objective was to meet this constraint with 4 mm steel 

consumption divided between the two components. 

A great performance improvement was achieved when the 

Opencell Delta concept was established in which four 

delta-shaped legs form the core. Also, it provided high 

unit cell packing density and consequently, rather 

homogenous structure. 

Fig. 3 - Bottom left: optimum layout for curved panel applications. Right: maximum displacement 

results as a function of the unit cell width for a flat panel (H 75 mm, span 3 m). Top left: optimum 

layout for the flat panel. 

OPTIMIZATION 

The next step in the project was to gather more 

information about the type of applications for which the 

Opencell Delta structure is appropriate and to determine 

the optimum cell configurations in the different 

applications. The design study was divided to flat and 

curved panel applications. Three corresponding panel 

heights were selected to represent different design groups 

(Table 1). Each design group was further on divided to 

three sub-groups determined by the span of the beam-like 

panel. In case of flat panels, two opposite edges were 

simply supported. For curved panels, also the longitudinal 

translations were constrained on both edges. In both 

applications, panels were loaded with a uniform pressure 

load of 4800 Pa. 

After the conceptual design studies it could be concluded 

that to achieve the best performance, certain geometrical 

measures need to be driven towards the minimum or 

maximum allowed value. As a result, free design variables 

were limited to the choice of the unit cell width and the 

thickness combination of the two face sheets. Allowed 

face sheet thicknesses for the two applications are 

presented in Table 1. 


In the optimization problem the objective 

was to maximize specific stiffness of the 

panel (inverse of the panel maximum 

deflection divided by the total thickness of 

the two sheets). 

The optimization procedure was set up in modeFRONTIER, 

the design optimization and process integration software 

package of our choice. The design of experiments and 

optimization algorithms provided in modeFRONTIER were 

used to drive the Opencell towards our goals. 

Thus modeFRONTIER created new designs by determining 

input values for the variables which were fed to a fully 

parametric FE model in ANSYS. Results for a single design 

study are presented in the 4-D image of Figure 3. 

Displacement results are presented as a function of the 

three variables for the different designs. 

For illustration, displacement results were 

selected because they are not as abstract 

as the specific stiffness. The unit cell 

width is on the horizontal axis. The color 

of the bubble indicates the total thickness 

of the face sheets. 

For example, cyan bubble (SUM_30…) 

indicates that the total amount is 3.0 

mm. The stiffest structure in terms of real 

displacements naturally comes with the 

highest amount of material. Therefore, red 

bubbles (5.0 mm) are on top (least 

negative values). In the study, the total 

amount of material was restricted to 5.0 

mm. The diameter of the bubble indicates 

the thickness of the plain sheet. The smaller the bubble is, 

the thinner the sheet. Therefore, for this application, the 

stiffest structure is obtained by maximizing the thickness 

of the cut sheet. This is the trend particularly for flat 

panels with long support spans. Increased thickness on 

the cut sheet moves the neutral plane closer to the 

geometrical mid-plane and therefore, panels work better 

in bending. For long span applications, bigger unit cell 

size is preferable as it increases the amount of material at 

the surfaces. For short span applications, which are outof-plane 

shear dominated, smaller unit cells are 

preferable. It should be noted that the plain sheet should 

be rigid enough to avoid local deformation close to the 

supports or loading points. 

Curved panels behave quite differently. They act 

essentially the same way as pressure vessels, where loads 

are carried by membrane forces. Therefore, the in-plane 

stiffness dominates the applications and the fewer cuts 

per unit area, the bigger the membrane area. The best 

specific stiffness is obtained with the combination of the 

thickest plain sheet and the thinnest cut sheet, and with 

the highest value of the unit cell width. Still, the cut 

sheet should be thick enough so that local deformations 

and stability are not a problem.


Fig. 4 - The software solution provides panel key properties such as EI and GA. User can solve the panel deflection U under different boundary and loading 

conditions. Stress recovery is made for a single unit cell with a separate model. 

SOFTWARE SOLUTION 

Opencell Delta simulation capabilities have been 

integrated as a separate module in ESAComp, a software 

tool for the analysis and design of layered composite 

structures. The software module consists of three 

components: geometry modeler, homogenization tool and 

simulation tool. 

The geometry modeler creates an FE mesh for a single unit 

cell according to the user-defined parameters (e.g. panel 

height, unit cell width, angle of the legs, etc.) and 

provides basic functions for visualization of the geometry. 

The homogenization tool calculates the basic properties of 

the Opencell Delta panel. In the conceptual design 

phase, designers would like to compare different 

alternatives with some key properties, like panel axial 

stiffness, bending stiffness, and shear stiffness, which are 

derived internally. The panel properties can be used in 

commercial FE codes to calculate deflections of flat and 

curved panels with arbitrary shapes, boundary and loading 

conditions. The homogenization approach is described in 

detailed in [6]. 

The simulation tool integrated in ESAComp software 

supports the analysis of flat panels (see Figure 4). The 

ESAComp solution relies on Elmer solver [7]. Two types of 

analyses are supported: static load response and analysis 

of details that covers, for example, failure analysis. 

CONCLUSIONS 

The introduced Opencell Delta concept provides a brand 

new way to construct metal sandwich panels. The concept 

gives potential for cost savings in manufacturing due to 

the reduced number of components and simplified 

continuous manufacturing process. In specific 

applications, increased mechanical performance can be 

achieved even with less material when compared to 

traditional metal sandwich panels, as could be shown 

optimizing Opencell Delta panels with modeFRONTIER. 

Both aspects are highly valued especially in 

transportation applications. Opencell panels can be 

formed in challenging shapes and they provide internal 

space for wirings, piping and other equipment, which may 

be required in the product. The presented software 

solution provides an efficient way to perform panel 

dimensioning. With a very limited effort one can reliably 

estimate if the Opencell Delta concept brings benefits in 

the specific application. 

REFERENCES 

[1]Säynäjäkangas, J. and Taulavuori, T. 2004. A review in 

design and manufacturing of stainless steel sandwich 

panels. Stainless Steel World, October, 2004. pp. 55 – 

59 

[2]Lohtander, M. & Varis J. P. A novel manufacturing 

process for producing cell structures using a modern 

turrett punch press. 17th ICPR (International 

Conference of Production Research), 3 to 7 August, 

2003. Blacksburg (VA), USA. 9 p. 

[3]Larkiola, J., Martikainen, H., Pellikka, E. Simulations of 

the forming and loading conditions of calotte panel 

structures. Espoo, 2003. VTT Technical Research Centre 

of Finland, report BTUO35-031181. 13 p. + 2 p. app. 

[4]Patent application WO/2009/034226. 2009. Panel 

structure. Outokumpu Oyj. Priority 11.09.2007, publ. 

19.3.2009. 23 p. 

[5]Gales, A., Sirén, M., Säynäjäkangas, J., Akdut, N., van 

Hoecke, D. and Sánchez, R. 2007. Development of 

lightweight trains and metro cars by using ultra-highstrength 

stainless steels. European Commission. Final 

report EUR 22837. 266 p. 

[6]Katajisto, H., Valente, A. and Mönicke, A. Designoptimisation 

of the innovative, high-performance 

metal sandwich solution. 9th International Conference 

on Sandwich Structures ICSS 9. California Institute of 

Technology, Pasadena (CA), USA, 14 to 16 June, 2010. 

[7]Elmer Models Manual, CSC - Scientific Computing Ltd., 

2007, Elmer web site www.csc.fi/elmer 

HARRI KATAJISTO, ANDRÉ MÖNICKE 

Componeering Inc., Itämerenkatu 8, FI-00180 Helsinki, 

Finland 

ANTONIO VALENTE 

PLY Engenharia, Lda, Largo dos Fornos 1, PT-2770-067 

Paço de Arcos, Portugal

I processi di forgiatura a stampi aperti (open-die) e di laminazione 

circolare (ring-rolling) sono in grado di produrre pezzi 

di grosse dimensioni in acciaio, il più possibile privi di porosità 

e con la massima omogeneità nelle caratteristiche 

meccaniche. Tali specifiche sono richieste nei settori meccanico-siderurgico 

(alberi pignone, pignoni, ruote pignone, alberi 

eccentrici, terminali e manicotti, …), petrol-chimico 

(corpi valvola, tubi, B.O.P., …), navale (alberi intermedi, alberi 

pinna ed alberi timone, …) e dell’energia (alberi turbina, 

alberi ventilatore, alberi per eolico, generatori quadripolari, 

…). In particolare in questo ultimo ambito, i componenti 

necessari per la costruzione di centrali nucleari devono 

soddisfare delle normative molto restrittive in grado di garantire 

le prestazioni del manufatto dopo molti anni di utilizzo 

in condizioni severe di contatto con agenti aggressivi e 

sottoposti ad irraggiamento. Si cerca quindi di produrre particolari 

con meno discontinuità (porosità, inclusioni, segregazioni, 

…) possibile e che possano essere assemblati limitando 

al minimo le saldature. Lo sviluppo integrato (la design 

chain) parte della colata dell’acciaio in acciaieria in una 

forma opportuna (lingotti, barre, blumi, altre forme), per proseguire 

con il trasferimento in forgia, dove il lingotto viene 

controllato, trattato termicamente e quindi lavorato alla 

pressa e/o al laminatoio. Il trattamento termico e le lavorazioni 

meccaniche di sgrossatura e di finitura sono le fasi conclusive 

per l’ottenimento del pezzo finito. Per ognuna di queste 

fasi esistono degli strumenti di simulazione dedicati e 

verticalizzati che consentono di simulare accuratamente le 

condizioni al contorno della fase in esame allo scopo di prevedere 

e ottimizzare la qualità del componente, grazie ad una 

migliore comprensione dell’influenza dei parametri di processo. 

L’approccio viene applicato e descritto successivamente 

nel caso di un lingotto in acciaio. 

Acciaieria - processo di colata 

Il processo di colata di lingotti è caratterizzato da una serie 

di parametri molto complessi, che determinano la qualità del 

prodotto finale. La composizione della lega è uno degli 

aspetti più importanti, ma lo sono anche le modalità di colata 

(velocità di colata, utilizzo di polveri isolanti, …) ed i 

materiali utilizzati per la lingottiera. Tutti questi aspetti sono 

tenuti in conto dai software di simulazione, che possono 

dare delle utili informazioni su quello che succede nella fase 

di riempimento dello stampo e di solidificazione del metallo. 

Software specifici in questo ambito (MAGMAsoft di Magma 

GmbH ad esempio) riescono a valutare, oltre ai ritiri in soli- 


Componenti forgiati di qualità 

necessitano di un approccio CAE 

integrato – esperienze di simulazione di 

processo nel campo Energia e Nucleare 

Fig. 1 – MAGMASOFT – simulazione del riempimento di un lingotto. 

NUCLEARE 

Fig. 2 – MAGMASOFT – simulazione della solidificazione di un lingotto. 

E ENERGIA CAMPO 

Fig. 3 – MAGMASOFT – distribuzione di temperature in una lingottiera. 

NEL PROCESSO DI 

Fig. 4 – MAGMASOFT – Difetti: macro ritiri, micro ritiri, moti convettivi, 

segregazioni. SIMULAZIONE


Fig. 5 – Forge - riscaldo in forno lingotto da 40t – temperature a cuore ed a superficie. 

dificazione, anche la formazione di porosità, la segregazione 

dei vari elementi, la presenza di cricche a caldo, … (Figure 

1, 2, 3, 4). 

Al termine della prima fase del processo produttivo, ci si pone 

come obiettivo quello di trasferire le proprietà microstrutturali 

e gli eventuali difetti alla fase successiva come avviene 

nella sequenza reale. L’ormai pluriennale esperienza maturata 

in EnginSoft sia nel CAE che nel processo manifatturiero 

ha permesso lo sviluppo di algoritmi specifici che agevolano 

il dialogo fra strumenti software commerciali di diversa natura 

e storia. 

Forgiatura – processo di riscaldo del lingotto 

Il lingotto prodotto in acciaieria viene trasportato alla forgia, 

dove viene inizialmente riscaldato in forno. Questo processo 

non è così semplice come sembra, in quanto bisogna adottare 

degli accorgimenti in modo da garantire un riscaldo uniforme 

cuore-superficie, guidando nel contempo le trasforma- 

Fig. 6 – Forge – possibili movimenti impostabili per ogni colpo/passata ed esempio blumatura. 

zioni che avvengono nell’acciaio aumentando la temperatura. 

Le dimensioni dei lingotti sono considerevoli, quindi bisogna 

modulare il forno in modo tale che l’acciaio venga portato 

gradualmente ad una temperatura attorno ai 700-800°C, che 

deve essere mantenuta per qualche tempo in modo da rendere 

omogeneo il cambio di fase e non creare eccessivi gradienti 

termici cuore - superficie, dopodiché si può procedere fino 

alla temperatura di forgia di 1150-1200°C, fino ad ottenere 

Fig. 7 – Forge – ricalcatura, blumatura, stondatura spigoli, ricalcatura di testa e martellatura. 

una uniformità di temperatura tra cuore e superficie. 

La simulazione del riscaldo in forno (fig. 5) può essere 

effettuata con software come Forge, dove può 

essere specificata una curva dell’atmosfera del forno 

e le temperature del lingotto possono essere monitorate 

attraverso dei sensori. L’utilizzo dell’ottimizzatore 

integrato in Forge può essere utile per calibrare 

i tempi di permanenza in forno: grazie a questo 

strumento, nel caso specifico di un lingotto da 

40t, si è compreso come il tempo di permanenza alla 

rima temperatura deva essere aumentato da 16 a 

24h, mentre alla massima temperatura si possono risparmiare 

ben 12h. 

Forgiatura – processi di deformazione open-die 

Il processo di forgiatura è caratterizzato dalla presenza di 

due elementi fondamentali: il manipolatore, che tiene il pezzo 

in posizione e ne guida gli spostamenti e le rotazioni, la 

pressa, che deforma il pezzo con diversi colpi e passate. La 

forma finale viene ottenuta infatti attraverso una serie di deformazioni 

localizzate sotto le mazze, con tempi di diversi 

minuti che comportano un raffreddamento dell’acciaio e la 

necessità di prevedere, soprattutto per pezzi di grosse dimensioni, 

delle ricalde in forno anche di diverse ore, per riportare 

il pezzo alla temperatura di lavorazione. Le difficoltà 

principali nella simulazione di questo processo sono la necessità 

di automatizzare la sequenza degli afferraggi da parte 

dei manipolatori, la sequenza dei colpi e delle passate, con 

le corrette rotazioni e traslazioni del pezzo e/o delle mazze. 

Fondamentale è la corretta definizione del 

materiale in termini di curve di deformazione 

a caldo e dalla corretta definizione delle 

caratteristiche della pressa idraulica utilizzata. 

Il software Forge è stato sviluppato 

in questi termini grazie all’apporto dei molti 

utilizzatori francesi che producono nel 

campo dell’energia: la sequenza di colpi e 

passate può essere definita con precisione 

(fig. 6), indicando quando entrano in funzione i manipolatori. 

Per ciascuno di essi si può specificare la zona di presa o e 

l’eventuale rigidezza degli afferraggi, che possono arretrare 

alla spinta del materiale. La geometria delle mazze e del lingotto 

di partenza vengono infine importate da CAD (formati 

.stl o .step). 

Durante la simulazione si può valutare come viene indotta la 

deformazione del materiale: quando la pressa esaurisce

Fig. 8 – Forge - chiusura delle porosità di colata con il processo di forgiatura. 

l’energia, la mazza si arresta e si passa al colpo successivo. 

Se il materiale è troppo freddo non si raggiungono le altezze 

di ricalcatura/blumatura desiderate, perciò dall’analisi delle 

curve di discesa si può capire quando sia necessario sospendere 

la lavorazione per riportare in temperatura il pezzo. 

Possono essere simulate pressoché tutte le lavorazioni che 

vengono effettuate in forgia: ricalcatura con mazza piana o 

con bocca, blumatura a stampi piani, curvi o sagomati, stondatura 

degli spigoli, ricalcature di testa (Fig. 7). Partendo da 

barre o blumi è possibile utilizzare solo due mazze verticali o 

macchine più complesse con 4 mazze, per il processo di martellatura 

rotante, o valutare in virtuale macchinari definiti 

solo sulla carta. 


la posizione nel lingotto di partenza, come è mostrato in fig. 

9. L’utilità di questo metodo consiste nella identificazione 

degli istanti in cui i difetti si generano e le relative cause per 

agevolare il tecnico nella ricerca della soluzione appropriata 

e efficace. 

Risulta quindi ovvia la possibilità di calibrazione virtuale delle 

fasi di forgiatura necessarie per l’ottenimento di anelli a 

sezione rettangolare o sagomati, che poi possono essere laminati 

(fig. 10). 

In questo caso si effettuano delle analisi 2D molto rapide, simulando 

le fasi di ricalcatura, sagomatura, punzonatura e foratura, 

come mostrato nella fig. 11 per un anello sagomato. 

La funzione di “chaining” consente impostare, calcolare ed 

analizzare in sequenza tutte le operazioni, trasferendo i risultati 

da una operazione alla successiva, fino all’ultima azione 

di tranciatura, nella quale si abilita la funzione di danneggiamento 

per seguire la separazione del fondo per effetto del 

punzone di tranciatura. 

Volendo rimanere nel campo della produzione di componenti 

per il settore nucleare, un altro processo comunemente utilizzato 

per l’allargamento degli anelli è la bigornatura: l’anello 

ottenuto come sopra illustrato, di sezione rettangolare, 

viene caricato su un mandrino ed una 

mazza che ne provoca la deformazione 

localizzata. La rotazione del mandrino 

tra un colpo ed il successivo consente 

di ottenere un allargamento graduale 

dell’anello, preservandone la lunghezza 

(Figura 12). 

NUCLEARE 

E 

Forgiatura – processi di laminazione 

circolare 

Fig. 9 – Forge – valutazione a ritroso posizione difetti attraverso l’uso di sensori. 

L’anello prodotto con le fasi sopra descritte 

può quindi essere laminato, per ottenere le dimensio- 

La simulazione viene utilizzata anche per valutare se la deni (allargamento) e/o la forma desiderata (sagomatura del ENERGIA 

formazione è in grado di compattare a sufficienza il materia- profilo). I laminatoi, che possono essere di diverso tipo, sole, 

partendo eventualmente da una distribuzione di porosità no costituiti generalmente da un rullo principale che induce 

proveniente dalla simulazione di colata del lingotto, come è la rotazione, da un mandrino, che spinge il materiale verso il 

mostrato in Fig. 8. 

rullo e da eventuali coni, che guidano l’altezza del profilo. CAMPO 

Tutti questi oggetti possono essere piani o sagomati. La si- 

Gli eventuali difetti nel forgiato sono ovviamente oggetti di mulazione di questo specifico processo risulta molto com- NEL 

indagine e lo studio può essere effettuato anche a ritroso, plessa per la limitata area di contatto tra mandrino, anello e 

ovvero definendo la posizione del difetto rilevato nel pezzo rullo, nella quale si concentra la massima deformazione e per 

finito e seguendone il movimento “a marcia indietro” fino alla cinematica guidata dall’allargamento dell’anello. L’elevato 

PROCESSO DI 

Fig. 10 – processo di ottenimento di anelli a sezione rettangolare da laminare SIMULAZIONE


Fig. 11 – Forge – 2D ricalcatura, punzonatura e tranciatura fondo per un anello sagomato. 

Fig. 12 – Forge – bigornatura anello. 

Fig. 13 – Forge – simulazione processo di laminazione circolare anello rettangolare e sagomato. 

Fig. 14 – Forge – simulazione trattamento termico: temperatura, % martensite e durezza HV. 

Fig. 15 – Forge – simulazione fase di immersione dell’anello in bagno di tempra. 

numero di giri infine rende il numero di calcoli da effettuare 

molto elevato con una mesh adattiva che si aggiorna in particolare 

nelle zone di contatto. In tal caso le architetture 

hardware con calcolo parallelo multi-core o multi-processore 

(cluster) permettono di ottenere dei risultati sufficientemente 

accurati tempi ragionevoli. Nello specifico della definizione 

delle cinematiche del mandrino e dei coni è possibile guidare 

gli stampi mediante le stesse curve di laminazione che 

l’operatore imposta sul software del laminatoio, riproducendo 

quindi in virtuale il comportamento della macchina reale. 

L’analisi del comportamento del materiale tra mandrino e rullo 

consente di modificare la curva di laminazione e risolvere 

i tipici difetti di forma di questo processo, 

presenti soprattutto per pezzi sagomati: 

fish-tailing, mancanze di profilo, 

rigetti di materiale. 

Forgiatura – trattamento termico 

Per tutte le tipologie di pezzi sopra trattate, 

dopo la fase di deformazione è 

sempre presente il trattamento termico 

per migliorare le caratteristiche meccaniche 

dell’acciaio. Il processo, che può 

essere anche molto articolato, si compone 

di un riscaldamento in forno alla temperatura 

di austenitizzazione ed una 

successiva immersione in un bagno di 

tempra, che induce delle trasformazioni 

microstrutturali nell’acciaio, modificando 

le fasi presenti. A seconda della drasticità 

dello scambio termico, si vanno a 

formare ferrite, perlite, bainite e, nella 

zone dove massimo è il gradiente, martensite. 

Nello specifico, la trasformazione 

martensitica è una trasformazione 

esotermica ed induce una espansione 

della struttura cristallina, che può provocare 

una distorsione del pezzo. Anche 

per questa operazione è possibile utilizzare la simulazione, 

con il software Forge, per valutare, al variare del percorso di 

tempra e della forma del pezzo la formazione delle varie fasi, 

in funzione del raffreddamento imposto alle varie zone, per 

confronto con le curve TTT del materiale. Viene infatti effettuato 

un calcolo termico-meccanico-metallurgico accoppiato, 

grazie al quale si ottengono le fasi e la conseguente durezza 

finale del pezzo (fig. 14). 

Recentemente il modello di calcolo è stato migliorato per tener 

conto di parametri di processo quali ad esempio la durata 

della fase di immersione nel bagno di tempra e l’effetto 

sulla tempra del pezzo, come è mostrato in fig. 15.

Fig. 15 – Forge – simulazione fase di immersione dell’anello in bagno di tempra. 

Una volta valutate tutte le singole fasi di produzione di un 

componente, dalla colata dell’acciaio, alla deformazione, alla 

tempra ed alle lavorazioni meccaniche, è possibile infine utilizzare 

i risultati ottenuti (distorsioni di forma, stress residui, 

proprietà meccaniche, difetti, …) per effettuare delle analisi 

strutturali o fluidodinamiche, nelle quali valutare le prestazioni 

in esercizio del componente. Nell’immagine seguente è 

mostrato come l’introduzione degli stress residui e delle proprietà 

meccaniche come condizioni iniziali per l’analisi strutturale 

modifichi in modo rilevante le caratteristiche meccaniche 

di un corpo valvola sollecitato in esercizio. 

Conclusioni 

La presente panoramica ha evidenziato come oggi sia possibile 

simulare tutte le operazioni necessarie alla produzione di 

un particolare in acciaio di grosse dimensioni, partendo dalla 

colata del metallo nel lingotto, alla successiva lavorazione 

di forgiatura o di laminazione, al trattamento termico. 

Aspetto saliente è la possibilità con questi diversi strumenti 

di valutare tutta la design-chain, in modo da essere in grado 

di comprendere le cause di un problema andando a ritroso 

lungo tutti i vari passaggi di produzione. 

FORGIATURA MAMÈ 

Abbiamo fondato il CRS (Centro di Ricerca e Sviluppo) con 

l'obiettivo di sviluppare il nostro know-how e migliorare le 

conoscenze sui nostri prodotti e sul processo produttivo. 

L'acquisto di FORGE il software di simulazione del processo di 

forgiatura e trattamento termico ha lo scopo di aiutare il CRS 

nella creazione di know-how. Grazie a questo software è 

possibile quindi analizzare il processo nel dettaglio, 

ottimizzando le fasi di fabbricazione, la qualità dei prodotti e 

quindi la riduzione dei costi e dei tempi - ciclo. L'ambizione 

dell'azienda è quella di riuscire ad offrire ai propri clienti un 

concreto strumento di cooperazione nella fase di 

progettazione dei prodotti, analizzando e simulando le 

caratteristiche che più soddisfano i requisiti che il forgiato 

deve possedere in funzione della sua destinazione d'uso. 

L'utilizzo di Forge rappresenta il punto più importante 

dell'attività del CRS: permetterà di acquisire una conoscenza 

oggettiva, di tipo scientifico-ingegneristico. Non più soltanto 

empirica e legata quindi solo all'esperienza personale di chi fa 

parte dell'azienda. 


Gli strumenti adatti e le competenze adeguate determinano 

un binomio vincente per lo studio e la progettazione di componenti 

High Tech forgiati per il settore energetico e in particolare 

quella nucleare. 

EnginSoft ha estese competenze nella simulazione di processo, 

derivanti da oltre 15 anni di esperienza a diretto contatto 

con questo tipo di problematiche del mondo industriale. 

Per informazioni, rivolgersi a: 

ing. Marcello Gabrielli – EnginSoft 


SOCIETÀ DELLE FUCINE – THISSEN KRUPP 

Società delle Fucine ha deciso di intraprendere la 

collaborazione con EnginSoft e dotarsi di strumenti di 

simulazione numerica del processo di fucinatura, scegliendo in 

particolare il software Forge nella versione parallela 

multiprocessore. La competenza e disponibilità dei tecnici di 

EnginSoft è risultata fondamentale per la rapida introduzione 

dei nostri parametri di processo e la taratura dei modelli 

numerici di fucinatura a stampi aperti che ha consentito di 

raggiungere simulazioni aderenti alla realtà in tempi molto 

rapidi. L'ing. Roberto Caldarelli, responsabile della 

preventivazione e della progettazione delle sequenze di 

produzione dichiara: “La scelta è caduta su questo programma 

grazie alla estrema flessibilità nella definizione delle 

cinematiche: tramite semplici istruzioni è possibile impostare 

le passate ed i singoli colpi, indicando il tempo di pausa tra 

un colpo ed il successivo e tutte le movimentazioni effettuate 

dal manipolatore per posizionare correttamente il pezzo sotto 

la pressa. Questi aspetti sono essenziali, assieme a risultati 

che abbiamo verificato essere molto precisi, per poter 

utilizzare Forge per prevenire possibili problemi di 

deformazione del pezzo sotto la pressa, adottando opportune 

modifiche dei cicli di stampaggio.“ “Prevediamo di utilizzare 

le simulazioni in modo via via sempre più sistematico per i 

nuovi pezzi prodotti e di estendere il suo utilizzo alle fasi di 

riscaldamento in forno, per prevedere tempi di riscaldo e 

dilatazioni e per il successivo processo di tempra, per valutare 

le deformazioni relative alla trasformazione martensitica, 

grazie alla possibilità di simulare le trasformazioni 

microstrutturali”. Per ultimo si cercherà un collegamento con i 

risultati ottenuti dalla simulazione della colata dei lingotti, in 

modo da tener conto delle caratteristiche proprie del lingotto 

di partenza e simulare tutto il processo produttivo. 

SIMULAZIONE DI PROCESSO NEL CAMPO ENERGIA E NUCLEARE


Landi Renzo: the global leader in the 

sector of components 

and LPG and CNG fuel systems 

Based in Cavriago (Reggio Emilia 

- Italy), with more than 50 years 

experience in the sector, Landi 

Renzo is distinguished by a 

sustained revenue growth, a 

listing in the STAR segment of 

the Italian stock exchange, and the extent of its 

international operations, with a presence in over 50 

countries. 

The Landi Renzo Company was established in 1954 when 

Renzo Landi and his wife Giovannina Domenichini founded 

Officine Meccaniche Renzo Landi, at the time the only 

manufacturer of mixers specifically designed for all kinds of 

vehicles. 

Landi Renzo S.p.A. is 

now a global leader in 

the sector of 

components and LPG 

and CNG fuel systems 

for motor vehicles, 

serving more than 

30% of the market of alternative automotive fuel systems 

and components. It is a preferred supplier by a growing 

number of worldwide brands like Daimler Chrysler, Fiat, Opel, 

PSA, Renault, Volkswagen, and more recently Toyota. 

Landi Renzo S.p.A. Research and Development Centre is 

currently the only one in its field to use advanced 

technologies that allow creating and developing modern 

systems to convert vehicle fuel systems to LPG and CNG. 

Visit the website on: www.landi.it 

modeFRONTIER in LandiRenzo 

“The first project with modeFRONTIER®, a product of ESTECO 

srl, dates back to 2008, when we performed an optimization 

of the new Electronic Pressure Regulator (EPR) - says 

Ferdinando Ciardiello, Research & Development Modelling 

Manager at Landi. “Ercole Sangregorio, current EPR Project 

Manager, - continues Ciardello - built-up a two steps 

development: at first, leveraging on experimental test data 

available in our in-house facilities, modeFRONTIER calibrated 

a numerical model of the EPR. We obtained a very precise 1D 

model, able to predict well and quickly the system’s behavior, 

the steady-state and the transient in different possible 

configurations. Afterwards, modeFRONTIER was used as a 

process integrator and a multi-objective optimizer, 

connecting different software tools to build a truly and 

multi-disciplinary virtual bench, with mechanical, pneumatic 

and control system models, and finding overall optimal 

configurations. In this way, we 

were able to minimize pressure 

oscillations in the control 

volume and to get an optimal 

and robust EPR configuration in 

just few weeks”. “Moreover, we 

expanded the concept to 3D 

fluid-dynamics design, 

particularly with the ANSYS 

Workbench direct node in 

modeFRONTIER, resulting in 

scheduled 3D simulation 

campaigns during night time 

and weekends. It proved to be a very efficient approach, 

based on the state-of-the-art Design Of Experiment available 

in modeFRONTIER.” 

Why modeFRONTIER and EnginSoft 

“Computer-Aided Engineering has always been a key success 

factor for our growth”, says Viliam Alberini, Leader of the 

Components Division, “and EnginSoft has been supporting 

our demands well for years. Adding modeFRONTIER to our 

software chain in 2008 has been a winning move for more 

than one reason: with modeFRONTIER our approach to 

product concept has become more systematic and now allows 

us to evaluate more alternatives and take into account the 

effects of more design variables. This translates into value to 

our customers: critical factors are understood and handled 

much earlier in the process and the design results more 

robust in a shorter time. modeFRONTIER has also improved 

the predictive power of our numerical models by feeding 

them with lab testing results. This philosophy has reduced 

development times and costs, and our team can cater to 

customer demands and discuss specifications with them more 

efficiently”.

Manufacturing companies are increasingly tending to 

centralize the management of their 3D product data. This 

requires moving from segmented 3D CAD data conversion 

and communication paths to a single integrated system. 

Such a system must be able to correctly translate product 

data from one CAD system to another and also be able to 

prepare the data for other uses, such as FEA and CAM. 

However, these tasks are not always done correctly or 

adequately due to the significant difficulties in handling 

mismatches between various software systems. Though 

this is a difficult undertaking, Nissan Motor Company – 

the well-known Japanese automobile manufacturer – has 

successfully built a reliable system for conversion and 

distribution of their 3D product data to be used companywide 

for all CAD-CAM operations. 

At Nissan, the control and delivery systems had used many 

3D tools in production technology, and the level of data 

quality had differed from tool 

to tool. This difference in 

quality frequently disrupted 

the accuracy of data 

translation. To solve this 

problem, Nissan launched a 

new project to shift to a 

totally new translation 

workflow, while changing its 

standard CAD system from Ideas 

to NX. 

On this project, Nissan chose 

‘ASFALIS’, one of the products 

of Elysium - a Japanese 

provider of 3D 

interoperability solutions - to 

consolidate the company-wide data conversion system in 

order to achieve high accuracy and great stability of 

performance. ASFALIS helps users establish a large-scale 

and flexible system to automatically operate CAD-to-CAD 

conversion or other optimization. It has been introduced 

among many Japanese major automobile manufacturers 

for their CAD conversion. 

“We are replacing all the 3D translators in production 

engineering with Elysium’s ASFALIS, which controls all the 

3D data translation and distribution processes in Nissan,” 

said Katsuro Fujitani, senior manager of the 

manufacturing and SCM system department in global IS 

division (as of 2010). He continued, “With its preeminent 

translation performance, more than 99.9 percent of the 


The CAD-CAM Cooperation in Nissan 

Achieved by ASFALIS 

data can be converted without error.” ASFALIS adapters 

are ready for all the possible translation patterns, which 

allow the Nissan staff to utilize any type of CAD data. In 

fact, they translate 3D data between INCAM* and several 

CAD systems such as I-deas, NX and DELMIA. Even large 

amounts of data are automatically converted and delivered 

to predetermined destinations. It is also able to control 

concurrently running file translation processes. Because 

the ASFALIS-based system is integrated to the intranet 

and connected with ‘Teamcenter’, the master PDM, Nissan 

staff in domestic branches are able to access ASFALIS to 

execute translations. The results of translations are 

automatically delivered in a specified format to another 

branch. 

Even though different paths are needed between approved 

data and data under consideration, users merely have to 

change settings. Once configured, ASFALIS automatically 

translates data and distributes results, whose quality is 

admirable and stable. Elysium’s reliable 3D data 

translation and distribution system has improved the 

efficiency at every step of processes throughout the 

product lifecycle management (PLM) in Nissan. 

* INCAM is the in-house CAM system in Nissan. 

For more information, please visit the ELYSIUM website: 

http://www.elysium-global.com 

For information on Elysium products in Italy, please contact: 

Giorgio Buccilli at EnginSoft, info@enginsoft.it


CAE-based tablet design 

Supported by 

Economic growth has provided us with many rewards including a 

wealthy and comfortable society. At the same time though, we 

are facing problems that occur with an aging population and an 

increase in lifestyle diseases. Even in Japan, the country with 

the highest life expectancy, health problems linked to lifestyle 

and age are evident. While in Europe, Japanese cuisine has 

become quite popular in the last decade, also for supporting a 

healthy diet and for curing disorders linked to an excessive 

lifestyle, in Japan a trend of eating more “fast” and less 

Japanese food can be witnessed. The consequences are increases 

in bad dietary habits and lifestyle diseases. Both have led to 

more frequent and longer sicknesses and to shortened life 

expectancy. In a modern society like this, pharmaceuticals are 

more and more in demand to offer supplements that can 

effectively treat our increasing health disorders and thus 

improve the quality of our lives. 

ASAHI BREWERIES GROUP is one of the largest food 

manufacturers in Japan. The Group mainly produces alcoholic 

beverages, but also focuses on the research and development of 

various supplements for the food production part of their 

business. Some of these supplements are designed for 

compensating the lack of a healthy nutrition, such as vitamins 

and minerals that tend to be insufficient in our modern diet. 

Some of these supplements include beer yeast for which the 

ASAHI BREWERIES GROUP has become world-famous. The Group’s 

supplements are of the highest quality and hence are products 

that we can trust. 

Pharmaceuticals and supplements are available in many forms, 

such as tablets, granulates, hard and soft capsules, jellies or 

syrups. Tablets are the most common today, and they come in 

many different shapes, colors and flavors. People sometimes find 

it difficult to swallow tablets because they must be taken 

without chewing and are usually washed down with hot or cold 

water (except for chewable tablets). 

It actually is an extremely important requirement today to 

develop tablets that are easy to swallow, both from a 

compliance and a usability point of view. Until recently, no 

quantitative research on the correlation between shape of tablet 

and ease of swallowing has been made. It was Mr. Hideaki Sato 

of ASAHI BREWERIES, LTD. who began to investigate the shape 

of tablets for ease of swallowing, using sensitivity engineering 

and optimization methods to develop the most suitable shape. 

Mr. Hideaki Sato evaluated the “tablet shape/hardness” and the 

“pressure resistance” of the tablet machine pestle using the FEM 

simulation software ANSYS. 

Study of the tablet’s shape for ease of swallowing 

To review the correlation between tablet diameter, radius of 

curvature, thickness and ease of swallowing of the most typical 

circular tablets, the following steps were carried out by using a 

sensory evaluation technology based on the experience of food 

development and response surface methods. 

Step 1: Preparing tablets 

36 different shapes of tablets made from microcrystalline 

cellulose and calcium stearate were prepared with all the 

possible combinations of tablet diameter (6mm, 7mm, 8mm and 

9mm), radius of curvature (6mm, 9mm and 12mm) and thickness 

(3 values from 2.5mm to 6.5mm). 

Fig. 1 - The shape of the circular tablet 

Step 2: Sensory evaluation 

The participants were 10 healthy men and women who took 

tablets from Step 1 with a glass of water every 30 minutes in 

random order. With the sensory evaluation, the ease of 

swallowing was rated on a 5 level score. 

Fig. 2 - The response surface of tablet diameter, radius of curvature and ease 

of swallowing

Step 3: Analysis of sensory evaluation result 

Based on the results of Step 2, the response surface of the tablet 

diameter, radius of curvature and ease of swallowing was 

established by spline interpolation connecting each data point. 

This was done for actual ease of swallowing and for apparent 

ease of swallowing. 

The result shows that it is easier to swallow when both the 

diameter and the radius of curvature are smaller with the 

smallest diameter and radius of curvature being the easiest when 

the thickness is 3.5mm. However, the best score for swallowing 

in cases where thickness exceeds 3.5mm is when the diameter is 

7mm, not the lowest value 6mm. This way, it became clear that 

the smaller diameter is not always better for swallowing. 

Moreover, it is important to consider the most appropriate 

diameter based on the radius of curvature and the thickness. 

Regarding the apparent ease of swallowing, the result was 

different from the result of the actual ease of swallowing. The 

participants felt that smaller diameters and smaller radiuses of 

curvature are generally better. Additionally, the best shape for 

swallowing based on each volume was specialized. This research 

revealed the relation between tablet shape and ease of 

swallowing. 

Considering the fact that it is indeed difficult to change 

pharmaceutical diameters in Japan, the conclusion was that the 

most appropriate solution would be to reduce the radius of 

curvature (i.e. to make the shape round). 

However reducing the radius of curvature size entails the 

problem of decreased durability of the tablet and the die (a part 

of the tablet machine pestle). To overcome this, ANSYS was used 

for the stress simulation of the arbitrarily-shaped tablet and 

tablet machine pestle. 

Stress simulation for the arbitrarily-shaped tablet 

Typically, in the pharmaceutical and food industries, the tablet 

strength is evaluated by a stress test to examine the fracture 

load under the unidirectional load of the tablet. This is called 

“Tablet Hardness”. To predict this as accurately as possible, CAE 

simulations were performed. We should mention here that this 

was the first time that a CAE approach has been applied in these 

industries. In fact, tablet strength evaluations are very 

challenging, as tablets are made of compressed formations of 

powdered substances and unlike mechanical structures, the 

shape and Young’s modulus are different and dependent on the 

pressure loads. 

Step1: Simulation under the assumption of a constant Young 

moduleus 

As a first step, the reaction force of the tablet, a 1/8 

symmetrical segment model, was calculated under enforced 

displacement. In this simulation, it was assumed that the 

tablet’s Young Modules was constant, and an adequate material 

property was defined for the model. It was done this way 

because 3 different shapes of tablets made by the same pressure 

load had almost the same volume (density), and the prediction 

was that the Young Modules would be constant. However, there 

was a divergence between the simulation results and the test 


results under the same conditions. So, it became obvious that 

applicable accuracy cannot be expected under the assumption 

that the Young Modules is constant. 

Fig. 3 - 1/8 model of the tablet and analysis condition 

Step2: Simulation with the Young Module defined by the reaction 

force on the pestle 

This new approach was applied to gain realistic values for the 

tablet’s Young’s Modulus. The stress simulation of the tablet 

machine pestle was performed to obtain the reaction force on 

the pestle head when tableting, and then to verify the 

distribution of the reaction force as the distribution of the 

tablet’s Young Modulus by transcribing it to the tablet model. 

Fig. 4 - The tablet machine pestle and the area of the simulation 

Fig. 5 - 2D symmetrical model for the simulation 

In order to determine the reaction force on the contact area, a 

stress simulation using the 2D symmetrical model shown in 

Fig.5, was performed. The model was made from chrome-nickel 

and a load of 20kN was applied to the upper region. The 

distribution of the reaction force was obtained by the simulation 

illustrated in Fig.6. 

The reaction force was then transcribed to the tablet model for 

its own stress simulation. At the same time, the average reaction 

force for each divided region (2 parts or 4 parts) was transcribed


Fig. 6 - The reaction force distribution on the pestle head 

Fig. 7 - Young Module transcription to the tablet model 

Fig. 8 - Relation between tablet hardness and reaction force 

to the similarly divided region of the tablet model (the 

transcription model of SATO-MIURA). 

The result of the ANSYS simulation was consistent with the 

experimental result, and it led to suitable results with practical 

accuracy. 

Fig. 9 shows the 3 different contour plots of the stress 

simulation for the tablet with a diameter of 8mm and a radius 

of curvature of 15mm. These are the results when the Young 

Modulus is assumed to be constant, divided into 2 parts and 

divided into 4 parts, respectively from left to right. 

The new approach to transcribe the reaction force on another 

model of the analysis model was applicable in this case. However 

further considerations regarding the applicable range (e.g. 

powder property, tablet machine type and tablet machining 

conditions) will become necessary in the future. 

Stress simulation for the arbitrarily-shaped tablet machine 

pestle 

The tablet machine is in operation all day to compress powder 

instantaneously with hundreds or thousands of kgf of pressure. 

A lot of stress occurs on the machine and sometimes this causes 

breakage. When reducing the tablet curvature size, the pestle 

head will be sharpened and the load capacity of the pestle will 

drop to a lower level. In the past, the load capacity of the pestle 

used to be based on the tablet machine manufacturer’s 

experimental rules. To predict the load capacity more accurately, 

an ANSYS simulation of the tablet machine was performed. A 2D 

axisymmetric model was prepared and the contact element 

between the pestle and the tablet was defined to represent the 

pestle sliding slightly on the tablet during the powder 

Fig. 10 - Analysis condition 

Fig. 9 - the maximum principle stress of the tablet with 8mm tablet diameter and 15mm radius of 

curvature 

compression phase. The surface of 

the tablet was defined as rigid. The 

analysis condition is shown in Fig. 

10. In this simulation, the 

calculation was repeated until the 

Young Module of the pestle: 

166,100Mpa 

Poisson ratio of the pestle: 0.3 

Acceptable stress value of 

metallic material of the pestle: 

2,172Mpa 

Tablet diameter: 6.0mm and 

8.0mm 

Land: 0.1mm 

Contact stiffness coefficient of 

the pestle and the tablet: 5.0 

stress value inside the pestle reached the allowable stress value 

of 2,172Mpa in order to know the load capacity. 

Fig. 11 shows the area which might break. This 

corresponds exactly with the tablet machine 

manufacturer’s experimental rule. Hence we can 

conclude that the CAE simulation for the tablet 

machine is valid. 

The new approach of using CAE received a 

great response from industry 

Today, CAE is the standard tool of machine 

design manufacturers and many examples,

Fig. 11 - Contour plot of the equivalent stress 

Comments from Mr. Sato of ASAHI BREWERIES, LTD. 

(Share the Kando.*) 

In today’s food industries, we only find a few examples 

for CAE- (and even fewer for FEM-) based product 

development. For the work described in this article, we 

applied ANSYS to evaluate our tablet design, and this 

attempt provided us with a lot of new and useful 

information. The response surface for the ease of 

swallowing has high prediction accuracy, this is why it is 

now used for product development in the ASAHI 

BREWERIES GROUP. Regarding the transcription model, 

the idea to consider the reaction force on the pestle as 

the tablet’s Young Module was a complete breakthrough. 

For the use of the approach in the future, we would like 

to make a decision on the applicable range of the 

theoretical model based on the powdered material and 

the working conditions of the tablet machine. 

We are no CAE specialists, so to us simulation is just a 

tool and not our main objective. It is necessary to 

cooperate with the CAE vendors for those simulation 

cases which cannot be solved by ourselves. In such 

situations, it is very important to deliver our analysis 

results and requirements to the CAE vendors as sufficient 

engineering knowledge and analytical thinking are 

indispensable. We expect from the CAE vendors that they 

don’t stick to their own technologies and simulation 

results, and that they provide flexible services. There are 

cases where – after sufficient communications and 

exchange of information - we find out that no use of CAE 

simulation is necessary. 

I am very pleased that Mr. Miura of Cybernet Systems has 

always responded quickly to my requests, and that he is a 

reliable partner. I do believe that the best solutions come 

from human communication, not from automatic 

computational calculation. This is the spirit of “Share the 

KANDO.” 

*This is the corporate message of ASAHI BREWERIES, LTD. 

It means: Always creating new value moves people’s hearts 

and forms a strong bond. Always imagining a fresh 

tomorrow moves people’s hearts and helps them shine. 

Sharing these emotional experiences with as many people 

as possible—this is the mission of the Asahi Breweries 

Group. 


reports and testimonials come from these industries. The 

specialists in the design and development divisions are 

becoming more and more familiar with CAE. The various 

technologies are not too difficult to apply, even for beginners, 

as they usually have previous experience, and the manuals and 

guidelines are pretty clear. Still, CAE with stress simulation has 

not been used in the pharmaceutical and food industries 

worldwide until recently. Mr. Sato indeed has made a big step 

forward with his idea of using CAE for tablet design. The key 

success factor was the new approach of using the substituted 

condition for the target simulation in cases where the real 

physical condition cannot be determined. In 2010, these series 

of simulations were presented at different academic meetings 

and in various publications. Mr. Sato’s work and approach 

received a great response from the pharmaceutical and food 

industries and the CAE sector. 

This article is based on the original case study by Mr. Hideaki 

Sato, Research Laboratories For Food Technology, ASAHI 

BREWERIES, LTD. and Mr. Takahiro Miura, Mechanical CAE Division, 

CYBERNET SYSTEMS CO.,LTD. 

Akiko Kondoh 

Consultant for EnginSoft in Japan 

Comments from Mr. Miura of CYBERNET SYSTEMS 

Co.,Ltd. 

CYBERNET SYSTEMS is a Japanese company offering 

computational engineering solutions, such as CAE 

software tools and all product-supporting services 

including seminars, support and consulting. ANSYS is one 

of our major business areas. We have a strong customer 

base in different industries, for example in automotive, 

electrical machinery, electrical devices, energy, aerospace 

and medical engineering. For the past 25 years since its 

establishment, CYBERNET SYSTEMS has been passionate 

about supporting MONOZUKURI in Japan as a CAE solution 

provider. Now, it also has some affiliated companies in 

Asia, North America and Europe, and has become a global 

player. 

ASAHI BREWERIES GROUP is a pioneering Japanese food 

and beverage manufacturer. We are truly honored to be 

able to collaborate with Mr. Sato who is promoting 

cutting-edge research and development. Currently, CAE is 

not used extensively in the food and beverage industries 

in comparison with other industries. We believe that CAEbased 

engineering simulation will provide effective 

solutions for achieving “time savings”, “cost reduction”, 

“security assurance” and “environmental protection”, 

important topics also for companies in these industries. 

We will endeavor to develop other examples for the 

engineers in the food and beverage industries, to 

encourage them to connect with and use CAE. We will also 

deepen the relations with our partners, like with Mr. Sato, 

and grow their passion for MONOZUKURI with our own 

passion.


Tokyo a Metropolis 

Tokyo, the capital of Japan, is the world’s biggest mega city 

according to the United Nations’ 2010 report, with a population 

of 13 million and 36.6 million if we include its surrounding 

urban areas. Another report by PricewaterhouseCoopers (PwC) 

states that Tokyo has the highest GDP of any cities in the world. 

Tokyo is also the heart of Japan with regards to politics, culture 

and education. When we think of Tokyo, images of typical 

cityscapes with high-rise buildings standing above busy crowds, 

elaborate train and subway systems, different varieties of 

academic and cultural facilities along with a rich entertainment 

heritage are conjured up. On the other hand, the city has many 

different aspects, such as numerous parks and green areas, 

waterways and finally the sea! There are many places where one 

can relax and unwind watching the changes of the seasons. From 

the many faces of Tokyo, I would like to introduce my favorite 

spots in this article. 

The skyscrapers 

When visiting Tokyo, many of my European friends are surprised 

by the cluster of high-rise buildings in different areas and the 

endless expansion of crowded residential areas spread across the 

suburbs. In fact, many new buildings are constructed with 

incredible rapidity every year, and the landscape changes 

The nightscape of Shinjyuku 

constantly. The high-rise buildings of Tokyo not only overwhelm 

people at daytime, they also present amazing views after sunset. 

Tokyo is known all over the world for its diversity of restaurants 

and gourmet places, in particular: Japanese, Italian and French 

cuisine are the people’s favorite. On the top floors of some tall 

buildings such as “Tokyo Midtown” in Roppongi and the 

“Marunouchi Building” near Tokyo station, we can enjoy the 

great bird’s eye view with a variety of gourmet food. This is truly 

a unique experience. After a busy day, a lot of people in Tokyo 

feel at home watching the illuminations and the slowly blinking 

lights floating into the night sky. 

The green oases 

Surprisingly, there are many large parks with a lot of trees in 

Tokyo. In Shinjyuku, located nearby the Tokyo Metropolitan 

March 2011 Earthquake 

and Tsunami in Japan 

This article was written before a terrible earthquake hit 

parts of Japan and its people. 

If you want to help, please donate to: 

Italian Red Cross: 

http://www.cri.it 

The Japanese Red Cross Society: 

http://www.jrc.or.jp/english/ 

British Red Cross: 

http://www.redcross.org.uk/ 

German Red Cross: 

http://www.drk.de 

or to any other organization that helps Japan in the 

present crisis. 

Thank you 

The Newsletter Editorial Team 

Shinjyuku-Gyoen Park 

Government offices, there is a park called Shinjyuku-Gyoen that 

I often visit with my family when the weather is fine. Shinjyuku- 

Gyoen is run by the Ministry of the Environment and covers an 

area of 580,000 m². This beautiful park invites visitors to enjoy 

gardens of three distinct styles: the French Formal Garden, the 

English Landscape Garden and the Traditional Japanese Garden. 

In spring, the park’s 1300 cherry trees attract many visitors as 

one of the best cherry blossom-viewing spots in Japan. A short 

distance from Shinjyuku, there is another large green oasis 

called Meiji-Jingu, it is the home of a famous Shinto shrine and 

covers 700,000 m². Meiji-Jingu is surrounded by a very old manmade 

forest. As soon as you enter the area, you will feel a 

sublime atmosphere, far from the hustle and bustle of the city. 

Once you have passed the wooden approach, you will reach the 

main hall enshrining a God, a treasure museum called 

Homotsuden and Shiseikan of martial arts. At week-ends, you 

might even be able to see a traditional Japanese wedding 

ceremony. 

Asakusa – the old town of Tokyo 

Dwarfed by the modern buildings, Tokyo’s old towns welcome 

visitors with warmth and old world charm. The most famous 

town is Asakusa which is very popular among foreign travelers.

Senso-ji Temple 

The Senso-ji temple can be found here, it is world-renowned for 

its Kaminari-mon. The existing main hall and five-story pagoda 

were reconstructed after having been burned down during the 

Second World War. The original buildings date back to more than 

a thousand years ago, and they are historical and symbolic 

temples of Tokyo. Along the approach from the Kaminari-mon to 

the Hozo-mon, there are many souvenir and snack shops lining 

up a street called Nakamise. Here we can enjoy shopping in a 

very Japanese atmosphere. The neighborhood is dotted with lots 

of classic restaurants, which will satisfy your appetite with very 

authentic Tokyoite dishes, such as Tenpura, Sukiyaki and Unagi. 

On the west side, there is Kappabashi, a street devoted to 

kitchenware, which supplies most of the restaurants in Tokyo. 

Recently, so-called ultra-realistic food models are sold here 

which have become very popular as souvenirs. 

Returning to the east side of the Senso-ji temple, there is the 

Sumida river, where you can enjoy a nice walk and maybe a short 

trip on a cruise boat. Behind the red painted Azuma-bashi 

bridge, the headquarters of ASAHI BREWERIES, LTD. are located, 

the Japanese Group that we introduce in this Newsletter with 

the CAE tablet design case study. It is a major landmark because 

of the golden flame on top of the black building. On the left of 

the ASAHI BREWERIES buildings, the world’s tallest TV tower 

“Tokyo Sky Tree” appears. It is still under construction. In 

February 2011, its height has reached 574m. The construction 

will be completed at the end of 2011, the final height will be 

634m. The number 634 was selected because of its 

pronunciation in Japanese which is MUSASHI, the old name of 

the Tokyo metropolitan area. 

The headquarters of ASAHI BREWERIES, LTD., and Tokyo Sky Tree 


ODAIBA the bay area 

Odaiba is the bay area which did not exist in the old maps 

because it was constructed by massive landfills towards the end 

of the last century, and the current landscape only appeared 

after the 1990’s. Towards the end of the Edo Period (1603- 

1868), a number of forts were built here on the different islands 

in the bay, to protect Tokyo against possible attacks from the 

sea. More than a century later, Tokyo began a spectacular 

development project aimed to relieve the congestion in the city 

center, and it became a large business and residential district 

starting with the opening of the Rainbow Bridge. The beautiful 

scenery of neo-futuristic streets and the bay area, attracts many 

visitors from all parts of the country to Odaiba. 

Odaiba is, at the same time, a favorite place for engineers. Many 

trade shows dedicated to the manufacturing and CAD/CAE 

industries are held at this “Tokyo Big Sight” which was opened 

in 1996. The hotels in this area are often chosen as venues for 

different Users’ Meetings of CAD/CAE software products. 

Rainbow Bridge in Odaiba 

Savor the Cuisine of Tokyo 

One of the major attractions of Tokyo is its cuisine and unique 

gastronomic variety. Here, locals and visitors from all parts of 

Japan and around the world, savor different kinds of Japanese 

food and the cuisines from many other cultures. In Tokyo, we 

can enjoy food of the highest qualities and standards at 

reasonable prices. 

If you visit Tokyo and wish to look for a suitable restaurant, 

there are several handy search guides you can use, for example 

GourNavi. You can select from a wealth of information based on 

location, food type and price range. 

If you search e.g., for a restaurant near Roppongi, about 2,000 

restaurants will come up very quickly (100 on the English 

website). In case you are tired at some stage, from all the going 

out to restaurants, why not discover DepaChika, the department 

store's basement food floor in the station. Here you can buy your 

favorite dishes from a variety of choices and take them to a 

nearby park or wherever you are staying – this is very easy and 

convenient and what the locals do also! 

Tokyo is one of the biggest and most modern metropolis in the 

world. At the same time, it is also a very interesting city that 

has always maintained its originality, a melting-pot of nature, 

people’s love for nature and traditional cultures… 

There is so much more about Tokyo that we will bring to you in 

the next Japan Columns of the Newsletter! 

Akiko Kondoh, Consultant for EnginSoft in Japan


President Obama Honors EnginSoft’s 

Partner with the Presidential Early 

Career Award for Scientists and 

Engineers 

Prof. Gianluca Iaccarino has been 

recognized last December with the 

Presidential Early Career Award for 

Scientists and Engineers (PECASE), the 

highest honor the U.S. government 

bestows on scientists and engineers in 

the early stages of their research careers, 

during an official ceremony at the White 

House in Washington D.C. Prof. 

Iaccarino, one of 13 U.S. Department of 

Energy researchers named as recipients, 

was recognized for “his extensive and 

deep scientific contributions in the areas 

of turbulent flow and uncertainty 

quantifications for the National Nuclear 

Security Administration community,” 

according to a Department of Energy 

official. The award winners were honored 

for their research efforts in a variety of 

fields, from helping the nation achieve 

energy independence to exploring the realms of space to 

identify dark matter. These awardees are funded by the U.S. 

Department of Energy's Office of Science and the National 

Nuclear Security Administration. The winning DOE scientists 

are among 85 researchers supported by 10 federal 

departments and agencies who have received the award. In 

addition to a citation and a plaque, each PECASE winner will 

continue to receive DOE funding for up to five years to 

advance his or her research. 

“Science and technology have long been at the core of 

America's economic strength and global leadership I am 

confident that these individuals, who have shown such 

tremendous promise so early in their careers, will go on to 

make breakthroughs and discoveries that will continue to move 

our nation forward in the years ahead” said President Obama. 

“These gifted young scientists and engineers represent the best 

in our country. The awards recognize ingenuity, dedication, 

diligence and talent. I congratulate the PECASE awardees and 

wish them continued success towards new discoveries and 

advances in science, energy research, and national security” 

said Secretary Steven Chu. 

The Award Motivation 

“For his extensive and deep scientific contributions in the 

areas of turbulent flow and uncertainty quantifications and 

quantified margins of uncertainty, which are 

amplified for the National Nuclear Security 

Administration (NNSA) community through 

his position of intellectual leadership at the 

NNSA Predictive Science Academic Alliance 

Program Center at Stanford” 

Nominated by Lawrence Livermore National 

Laboratory 

Prof. Gianluca Iaccarino 

Dr. Gianluca Iaccarino is an Assistant 

Professor at Stanford University with joint 

appointments in the Mechanical Engineering 

Department and the Institute for 

Computational Mathematical Engineering. He 

completed his graduate studies in Italy 

working on computational methods for fluid 

dynamics and worked as a Research 

Associate at the NASA Center for Turbulence 

Research before joining the Faculty at 

Stanford in 2007. He is the Deputy Director of the NNSA 

Predictive Science Academic Alliance Program (PSAAP) Center 

at Stanford and leads the effort on Quantification of Margins 

and Uncertainties, a decision-making computational 

framework aimed at managing risks associated to highconsequence 

systems. His research activities are focused on 

Computational Fluid Dynamics, in areas ranging from analysis 

of wind turbines, to hypersonic propulsion, to turbulence and 

transition modeling, to thermal management in batteries. In 

2007 Dr. Iaccarino funded the Uncertainty Quantification Lab 

(http://uq.stanford.edu): a joint initiative between the 

School of Engineering and the Mathematics and Statistics 

Departments. The UQLab is supported by various grants from 

NNSA, DOE Office of Science, NSF, and industries and focuses 

on probabilistic algorithms 

for uncertainty analysis, 

stochastic inference and 

robust optimization. The 

research work ranges from 

the theoretical aspects of 

uncertainty representation, 

to algorithms for nondeterministic 

analysis, to 

large-scale applications 

leveraging massively parallel 

computers. Many of the 

current projects involve

active collaborations with Sandia, 

Lawrence Livermore and Los Alamos 

National Laboratories. Dr. Iaccarino is 

involved in various educational 

activities at Stanford and in the 

computational engineering community. 

He organized Uncertainty 

Quantification tutorials, workshops and 

special sessions at major engineering 

conferences. He has published more 

than 50 papers in both engineering 

and mathematics journals and about 70 

conference papers. He is also a 

Humboldt fellow at the University of 

Munich, Germany. 

Dr. Iaccarino is also the Director of the 

Thermal and Fluid Sciences Affiliates 

and Sponsors Program (TFSA - 

http://www.stanford.edu/group/tfsa/) which EnginSoft has 

recently joined and he is also one of the co-founders of 

Cascade Technologies Inc. 

(http://www.cascadetechnologies.com), EnginSoft’s Partner 

Company in Palo Alto (California) that develops, markets, and 

supports state of the art Computational Fluid Dynamics (CFD) 

analysis tools for engineering applications across industries. 

About the Award 

The Presidential Early Career Award for Scientists and 

Engineers (PECASE) is the highest honor bestowed by the 

United States government on outstanding scientists and 

engineers in the early stages of their independent research 

careers. The White House, following recommendations from 

participating agencies, confers the awards annually. To be 

eligible for a Presidential Award, an individual must be a U.S. 


citizen, national or permanent resident. 

Winning scientists and engineers receive up 

to a five-year research grant. 

History 

In February 1996, the National Science and 

Technology Council (NSTC), was 

commissioned by President Bill Clinton to 

create an award program that would honor 

and support the achievements of young 

professionals at the outset of their 

independent research careers in the fields of 

science and technology. The stated aim of 

the award is to help maintain the leadership 

position of the United States in science. 

Originally, 60 recipients received the PECASE 

award per year. Due to increased 

participation by the Department of Defense, 

this has increase to 100 per year. The 2002 PECASE awards 

were not announced until May 2004 due to bureaucratic 

delays within the Bush administration. 

Agencies 

The agencies participating in the PECASE Awards program 

are: Department of Agriculture, Department of Commerce, 

Department of Defense, Department of Energy, Department of 

Education, Department of Health and Human Services: 

National Institutes of Health, Department of Veterans Affairs, 

National Aeronautics and Space Administration, and the 

National Science Foundation. 

From Wikipedia, the free encyclopedia: 

http://en.wikipedia.org/wiki/PECASE


Formazione a distanza sugli 

elementi finiti 

EnginSoft ha messo a punto e sostiene, per conto di Consorzio 

TCN, l'iniziativa di formazione a distanza in ingegneria “improve.it”, 

con l’obiettivo di creare una risorsa di alta formazione 

continua, coerente con gli obiettivi di formazione del Consorzio. 

Tramite il portale all’indirizzo http://www.improve.it è possibile 

accedere a corsi multimediali di auto-formazione sui temi della 

simulazione numerica e prototipazione virtuale e temi a questi 

complementari e affini. 

Nel 2010 il portale improve.it è stato completamente rinnovato 

sia nella grafica che nei contenuti. Tra le nuove funzionalità offerte 

da improve.it sono disponibili il nuovo sistema di ricerca 

dei contenuti e la nuova procedura online per l’acquisto dei corsi. 

Il contenuto di tutti i corsi disponibili è inoltre stato migliorato 

e per la loro erogazione viene ora utilizzato un nuovo sistema 

multimediale di elevata qualità. 

Il catalogo dei corsi è costantemente aggiornato e riportato sul 

sito. Per ogni corso è disponibile il programma dettagliato. È 

inoltre consentito l'accesso gratuito ad alcuni corsi. Sono oggi 

disponibili on-line corsi a vari livelli relativi a metodo degli elementi 

finiti, analisi statica e dinamica, fluidodinamica computazionale, 

acustica computazionale, elettromagnetismo, progettazione, 

ottimizzazione multi-obiettivo, scienza dei materiali, processi 

produttivi, metallurgia… 

Nuovo corso online 2011 di introduzione al FEM 

Il metodo degli elementi finiti: teoria e applicazioni meccanico-strutturali 

in campo elastico lineare 

Docente del corso: Prof. Leonardo Bertini, Dipartimento di 

Ingegneria Meccanica, Nucleare e della Produzione, Università di 

Pisa. Il corso, suddiviso in tre unità didattiche per un totale di 

19 moduli multimediali, si propone di fornire gli strumenti teorici 

e applicativi per l'impiego corretto e ragionato del metodo 

degli elementi finiti per lo studio di strutture meccaniche in 

campo elastico lineare. 

Prima unità didattica: 

le basi teoriche del metodo degli elementi finiti 

Vengono sviluppati otto moduli per introdurre la teoria del metodo 

degli elementi finiti, in modo semplificato e attraverso 

l'utilizzo di numerosi grafici ed esempi. Gli argomenti trattati includono: 

discretizzazione, campo di spostamenti, calcolo delle 

deformazioni, analisi agli elementi finiti, vincoli e carichi, fun- 

Istantanea di una delle lezioni multimediali che compongono il corso 

zioni di forma, convergenza della soluzione, utilizzo di elementi 

di ordine superiore al primo. 

Seconda unità didattica: applicazioni del metodo FEM alle principali 

classi di problemi strutturali in campo elastico lineare 

Nei successivi dieci moduli del corso vengono passate in rassegna 

le principali famiglie di elementi finiti e per ciascuna di esse 

vengono forniti esempi di applicazione e limiti di utilizzo. In 

particolare vengono affrontati i seguenti tipi di elemento: asta, 

trave, pipe, piani, di Fourier, gap, guscio assialsimmetrico, elementi 

guscio/piastra 3D, brick. 

Terza unità didattica: analisi critica dei risultati di un modello 

FEM e criteri generali di modellazione con il metodo degli elementi 

finiti 

I due moduli conclusivi del corso affrontano i criteri generali di 

modellazione FEM e introducono l'analisi critica dei risultati attraverso 

esempi sui seguenti argomenti: singolarità dello stato 

di tensione, definizione e schematizzazione dei vincoli, utilizzo 

delle simmetrie. 

Materiali aggiuntivi e questionario di autovalutazione 

Oltre al materiale multimediale della durata complessiva di 4 

ore, il corso offre tracce di esercizi da svolgere tramite analisi 

agli elementi finiti, e un questionario di autovalutazione della 

comprensione degli argomenti trattati, composto da 20 domande 

a risposta multipla. Agli iscritti che affronteranno positivamente 

il questionario di vautazione verrà inviato l'attestato di 

partecipazione al corso. All'interno del corso è disponibile il forum 

privato per porre domande al docente. Agli iscritti al corso, 

oltre all'accesso ai materiali didattici, verranno inviate le dispense 

a colori con la riproduzione delle oltre 280 diapositive 

utilizzate dal Docente nelle lezioni. 

Per informazioni e iscrizioni: 

http://www.improve.it

The new book by Roberto Battiti and Mauro Brunato is 

now available: 

ROBERTO BATTITI AND MAURO BRUNATO. 

Reactive Business Intelligence. 

From Data to Models to Insight. 

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ISBN: 978-88-905795-0-9 

Take the plunge into 

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Reactive Business Intelligence is much more than “pretty 

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The concept is illustrated in the figure RBI: reactive 

business intelligence. This holistic and unifying goal 

requires collecting and integrating topics which are 


REACTIVE BUSINESS INTELLIGENCE 

From Data to Models to Insight 

usually dissected in books 

dedicated to different areas. 

Brevity and attention to the 

essential ideas and methods 

were our design principles. 

Readers of the EnginSoft 

Newsletter deal with 

engineering simulations, 

models and designs and do 

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understand the powerful 

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We hope that this book will be useful to researchers and 

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The School of Athens, by the Renaissance artist Raphael, 1510. 

Last but not least, using proper visualizations can provide 

us with aesthetic satisfaction and even artistic emotions, 

although maybe not to the same extent as Raffaello’s “The 

School of Athens”… 

To buy this book, please visit the book’s web page: 

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By inserting the coupon code ENGINSOFT-RBI, a special 

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2011).


EnginSoft at the Optimization Day: 

Research and Applications 

EnginSoft joins the Thermal and Fluid Sciences Affiliate 

Program of Stanford University and Sponsors a One Day 

Seminar on Optimization. 

EnginSoft has recently strengthened its North American 

operations by means of expanding both the 

office’s space and the employed personnel at its 

Silicon Valley office, located at the Palo Alto 

Technology Center (San Francisco Bay Area - 

California). 

With the aim of stimulating the networking in 

the area as well as of providing continuous 

support and commitment to scientific research, 

EnginSoft has also joined the TFSA (Thermal and 

Fluid Sciences Affiliate) of Stanford University 

and has been welcomed as a new member 

during the 2011 TFSA conference which took 

place at the Munger Conference Center, inside 

the Stanford University Campus, in Palo Alto on 

February 2-4, 2011. 

The conference is organized every year within 

the TFSA program and is aimed at presenting 

the latest research work of the Stanford Thermal 

and Fluid Sciences program. This year’s 

conference has been enriched and anticipated 

by a One-Day Seminar on Optimization (the 

“Optimization Day”) which has been held on 

February 1st, at Stanford Campus. The event, as 

part of TFSA program, has been organized by 

Prof. Gianluca Iaccarino and EnginSoft ’s staff 

in Palo Alto. EnginSoft has indeed sponsored 

and fully supported the event with the intent of 

stimulating the discussion on optimization as 

an effective and practical means for 

engineering practice, while bringing its 

contribution in terms of leading technology 

(modeFRONTIER) and expertise in the field of Multi- 

Objective Design Optimization, in particular with respect 

to Computational Fluid Dynamics (CFD) applications. The 

many applications of optimization that were presented

anged from Rapid Product Development to Web searching, 

from Sophisticated Multidisciplinary Analysis to Robust 

Design Under Uncertainty. In each specific presentation 

the following questions were addressed: What are the 

Remaining Barriers for Optimization Algorithms? How are 

Present Computational Resources Changing the Paradigm 

of Engineering Design? Are Current Optimization Methods 

Sufficient to Drive Decision-Making? The objective of 

bringing together Stanford faculty and industrial 

representatives to discuss the 

current applications and 

remaining bottlenecks to the 

adoption of Optimization 

Algorithm was achieved 

through this event with a 

great success of attendance. 

Companies like Rolls Royce 

and GE illustrated their 

activities on aeroengines, 

while Ferrari brought its 

experience from its Formula 1 

racing activities. Stanford 

faculty members illustrated 

their wide experience in 

aerodynamic design via 

control theory (Prof. A. 

Jameson) and gave talks on 

frontier topics such as optimal 

design under uncertainties 

(Prof. G. Iaccarino). 

The following 3 days (2-4 

February) the TFSA convened 

for its annual conference, 

covering advanced topics in 

CFD introduced by Professor 

Parviz Moin. The conference is 

the main event organized 

within the program every year 

in February. It was an exciting 

conference presenting the 

latest work of the Stanford 

Thermal and Fluid Sciences 

program. The 2011 conference 

had over 100 participants, 


including affiliates representatives, sponsors of 

research, and special guests, and the technical program 

was made up of more than 50 oral presentations and a 

poster session. Several topics were covered with 

contributions in various areas of our research activities, 

including predictive science, aeroacustics and noise, 

fluid mechanics, LES, combustion (modeling and 

diagnostic) heat transfer, energy science, processing 

and engineering of materials, micro-scale flow and heat 

transfer and fuel cells, as well as issues regarding design 

Optimization under design uncertainties. Several new 

initiatives that are providing substantial growth in the 

research activities and new opportunities for industrial 

collaboration were described. During the last day of the 

conference, a lab tour was organized so as to illustrate the 

capabilities and state of the art of the Stanford labs and 

computing facilities. Finally, a dinner banquet at the 

Stanford Faculty Club was held; it was there that the best 

presentations and scientific papers were awarded 

(http://www.stanford.edu/group/tfsa/). 

Examples from Some Papers Presented at the 

TFSA Conference 2011 

“Large-Eddy Simulation of Active Flow Control”, by 

Parviz Moin (Stanford Univ.) and Arvin Shmilovich 

(Boeing) 

LES of Supersonic Jets from Complex Nozzles“ by 

Joseph W. Nichols, Frank E. Ham, Yaser Khalighi, 

Sanjiva K. Lele, and Parviz Moin


NAFEMS World Congress 2011 - 

Preliminary Agenda Announced 

International Association for the 

Engineering Analysis Community 

Releases Line-up for Global Simulation 

& Analysis Conference 

GLASGOW, UK, FEBRUARY 18TH 2011 – 

NAFEMS, the International Association for 

the Engineering Analysis Community, has 

announced the preliminary agenda for its 

2011 World Congress, being held in 

Boston, MA, USA between May 23rd and 

26th. Including over 150 presentations in more than 40 

sessions over three days, this represents the most 

comprehensive and wide-ranging collection of analysis and 

simulation specific papers available from one independent, 

international event. 

Keynote speakers at the Congress will include; 

Marc Halpern: Gartner, Inc., USA 

Mike Hinton: QinetiQ, United Kingdom 

Alexander Karl: Rolls-Royce Corporation, USA 

Ronald Krüger: National Institute of Aerospace, USA 

Wiley Larson: Stevens Institute of Technology, USA 

Laura Michalske: Procter & Gamble, USA 

The full agenda is available to view, and to download, from 

the Congress website at www.nafems.org/congress. 

Registration is also available here, as well as full details of 

the location, venue and exhibition opportunities. 

About NAFEMS 

NAFEMS is a not for profit organization aimed at 

promoting best practices and fostering education and 

awareness in the engineering analysis community. In line 

with its objectives to promote the effective use of 

simulation technologies, NAFEMS is continually seeking 

to create awareness of new analysis methodologies, 

deliver education & training, and stimulate the adoption 

of best practices and standards by offering a platform for 

continuous professional development. For more 

information, visit www.nafems.org. 

Further information and high-resolution images are 

available on request. 

Tim Morris from NAFEMS is available for further comment 

by arrangement. 

A number of short training courses and special workshops will 

also be available for delegates to attend, ensuring that their 

time in Boston is used to maximum effect. 

As many as 6 parallel tracks will run over the three days of 

the Congress, covering topics including; 

Optimization 

Integration 

Composites 

Materials 

CFD 

Fatigue & Fracture 

Geotechnics 

MBS 

Industrial Applications 

Business Benefits 

Dynamics & Testing 

Education 

Analysis Management 

Simulation Data Management 

Seismic Analysis 

High Performance Computing 

Business Benefits of Simulation 

…and many more 

The NAFEMS World Congress is the only event dedicated to 

showcasing the state-of-the-art and state-of-practice in the 

simulation world in an impartial forum, open to everyone 

with an interest in how to get the most from their use of 

simulation. Visit the Congress website at 

http://www.nafems.org/congress to find out more, and to 

register for the only independent, international conference 

dedicated to analysis and simulation technology. 

Press Contact 

David Quinn (NAFEMS), 

+44 (0) 13 55 22 56 88 david.quinn@nafems.org

EnginSoft alla Fiera 

Made in Steel di Brescia 

Dal 23 al 25 marzo 2011 si è svolta presso il centro fiera 

Brixia Expo Fiera di Brescia la quarta edizione di Made in 

Steel, un evento biennale dedicato alla filiera dell’acciaio. 

Made in Steel 2011 ha registrato un record di visite rispetto 

alle sue precedenti edizioni: 13.500 visitatori, provenienti da 

46 nazioni, confrontati con i 12.000 della scorsa edizione nel 

2009. Un aumento rilevante anche nel numero degli espositori, 

salito da 187 a 248, e di conseguenza nell’area espositiva 

(da 7.400 a 10.200 mq). Va ricordata inoltre la presenza 

delle delegazioni estere di Austria, Bielorussia e Cina. 

EnginSoft ha partecipato a Made in Steel con uno stand, focalizzando 

l’attenzione sui software e le applicazioni specifiche 

per il settore metallurgico, in particolare quelle sostenute da: 

MAGMA (MAGMAsteel e MAGMAfrontier), Transvalor (FORGE) e 

Third Wave Systems (AdvantEdge FEM e Production). 

La fiera è stata visitata prevalentemente da manager e il nostro 

stand ha registrato un buon afflusso di persone appartenenti 

ad importanti aziende con le quali sono stati organizzati 

degli incontri riservati dove sono stati discussi problemi 

specifici e ipotesi per future collaborazioni. 

Il terzo giorno, venerdì 25 marzo, la nostra società ha organizzato 

presso la Sala Steel il seminario dal titolo: “Energia 

Nucleare di nuova generazione: engineering e design di processo 

e di prodotto”. I relatori sono stati Piero Parona, che 

ha illustrato la mission di EnginSoft e le proprie referenze, 

Marcello Gabrielli le applicazioni di FORGE dedicate ai forgiatori 

d’acciaio, Enrico Borsetto quelle di ADVANTEDGE per le 

lavorazioni meccaniche, Gianluca Quaglia quelle di MAGMA 

dedicate ai processi di fonderia e di Trattamento Termico e 

Massimo Galbiati quelle FEM di tipo fluidodinamico, termico 

e strutturale. Erano presenti una cinquantina di partecipanti 

provenienti da importanti aziende fra le quali ThyssenKrupp, 

il Gruppo Cividale, il Gruppo FOMAS e Hydromec, importante 

costruttore di presse bresciano col quale EnginSoft ha siglato 

un accordo di collaborazione tecnologica che porterà 


Hydromec ad utilizzare FORGE per studiare nuove soluzioni di 

stampaggio e alla proposta congiunta di seminari tecnici per 

specifici settori dello stampaggio. 

Nel complesso Made in Steel è stata un’esperienza positiva 

che ha dato modo ad EnginSoft di farsi conoscere maggiormente 

nel settore metallurgico, dove è presente da circa dieci 

anni con clienti molto importanti, ma che può essere ulteriormente 

sviluppato. 

Hydromec srl ed Enginsoft hanno siglato 

un accordo di collaborazione tecnologica. 

Brescia 1 Marzo 2011 – Hydromec srl, azienda di riferimento 

nella progettazione e costruzione di presse per lo 

stampaggio dei metalli ed Enginsoft, società italiana di 

maggior consistenza e tradizione nel settore della 

sperimentazione virtuale e del CAE, hanno siglato un accordo 

di collaborazione tecnica finalizzato all'integrazione e 

sviluppo delle proprie tecnologie. 

Il processo di stampaggio dei metalli rappresenta oggi un 

settore manifatturiero di grande importanza e ad alto 

contenuto di sviluppo potenziale e, per questo motivo, le 

due Società hanno deciso di condividere know-how ed 

esperienze. 

Da una parte la tecnologia meccanica, la qualità dei 

materiali e l'accuratezza di progettazione con sistemi 

innovativi di controllo e gestione di Hydromec srl, dall'altra 

software di simulazione, come Forge e Ansys, e di 

ottimizzazione dei parametri di processo come 

modeFRONTIER, rappresenteranno una base innovativa per 

risolvere le problematiche e le applicazioni tecnologicamente 

sempre più complesse che i processi di stampaggio oggi 

richiedono. 

Hydromec srl 

Fondata nel 1980, Hydromec srl nasce 

come azienda per la revisione di 

macchine. 

Ben presto Hydromec srl dirige i propri sforzi produttivi verso 

il settore dello stampaggio a caldo dell'ottone. Nascono così 

le presse della serie HF che si avvalgono di ben dieci brevetti 

tecnici. Successivamente Hydromec srl amplia la propria 

gamma di prodotti realizzando le presse oleodinamiche a 

quattro colonne della serie HSF utilizzate nel settore della 

forgiatura dell'acciaio a caldo, cui si aggiungono i laminatoi 

della serie LAR per la produzione di anelli, flange e sagomati 

in acciaio. Il Sistema Gestione Qualità di Hydromec è 

conforme alle norme UNI-EN ISO 9001:2008. 

Per informazioni e contatti: www.hydromec.it


EnginSoft Event Calendar 

ITALY 

EnginSoft is pleased to announce the next Seminars and 

Webinars. For more information, please contact: 

eventi@enginsoft.it 

Please visit www.enginsoft.com 

EnginSoft International Conference 2010 

CAE Technologies for Industry. 

To receive a copy of the The Conference Proceedings, please 

contact: eventi@enginsoft.it 

EnginSoft International Conference 2011 

CAE Technologies for Industry 

Fiera di Verona - ITALY 20-21 October 2011 

Please stay tuned to www.caeconference.com for one of the 

major events for CAE Users in Europe! 

FRANCE 

EnginSoft France 2011 Journées porte ouverte 

dans nos locaux à Paris et dans d’autres villes de France, en 

collaboration avec nos partenaires. 

Pour plus d'information visitez: www.enginsoft-fr.com, 

contactez: info.fr@enginsoft.com 

Webinars Flowmaster: Introduction au logiciel Flowmaster 

31 March 

14 April 

18 May - Etats Généraux Micado. France 

http://www.enginsoft-fr.com/events/index.html 

15-16 June - Séminaire modeFRONTIER au CETIM 

Cetim Senlis. 


20-26 June - Salon du Le Bourget - Paris Air Show 

Le Bourget, Paris. 

Talk to our experts at the EnginSoft/ Flowmaster booth! 

http://www.paris-air-show.com/en 

18-29 June – Teratec Conference. Ecole Polytechnique 

Palaiseau. Meet us at the EnginSoft / Flowmaster booth! 


12 October - User Group Meeting modeFRONTIER France. Paris 


13 October - User Group Meeting Flowmaster France. Paris 


GERMANY 

Please stay tuned to: www.enginsoft-de.com 

Contact: info.de@enginsoft.com for more information. 

modeFRONTIER Seminars 2011. EnginSoft GmbH, Frankfurt 

am Main. Attend our regular Webinars and Seminars 

to learn more on how design optimization with 

modeFRONTIER. 

can enhance your product development processes 

14-15 April - Efficient Design of Composite Structures – 

ESAComp Users' Meeting 2011. Technical University of 

Munich, Institute for Carbon Composites 

EnginSoft will be presenting: Optimization and robustness of 

composite structures: The whole design chain driven by 

modeFRONTIER 

In addition to presentations on simulation and design of 

composite structures, the latest advances in the ESAComp 

software will be presented. Three workshops will be held: - 

the aerospace industry, - wind, marine energy and industrial 

applications, - optimizing composite structures. 

EnginSoft is a sponsor of the Users’ Meeting 

http://www.enginsoft.com/events/esacomp_um.pdf 

www.esacomp.com 

Seminars Process Product Integration 

EnginSoft GmbH, Frankfurt am Main 

How to innovate and improve your production processes! 

Seminars hosted by EnginSoft Germany and EnginSoft Italy 

SPAIN 

Programa de cursos de modeFRONTIER and other local events 

Please contact our partner, APERIO Tecnología: 

info@aperiotec.es 

Stay tuned to: www.aperiotec.es 

10 March - National Instruments Day - Barcelona. Discover 

the latest trends in technology and new products from 

National Instruments during the Technology Forum on 

Graphic Design Systems. 

AperioTec and ESTECO will be present to show the latest dedicated 

connection between modeFRONTIER and NI LabView. 

Participation is free. http://www.ni.com/nidays/es/ 

5-8 June - IDDRG 2011 International Conference. Bilbao (País 

Vasco). This year, in addition to stamping, material characterization, 

numerical simulation and tooling normally covered, 

the organizers would like to focus the conference on su-

stainability: of global concern, not only for industry but also 

for consumers, politicians and business leaders. 

For more information, please visit: 

http://www.iddrg2011.eu/ 

SWEDEN 

2011 Training Courses on modeFRONTIER - Drive your designs 

from good to GREAT EnginSoft Nordic offices in Lund, Sweden 

The Training Courses are focused on optimization, both multi- 

and single-objective, process automation and interpretation 

of results. Participants will learn different optimization 

strategies in order to complete a project within a specified 

time and simulation budget. 

Other topics, such as design of experiments, metamodeling 

and robust design are introduced as well. The two day training 

consists of a mix of theoretical sessions and workshops. 

7-8 April 

2-3 May 

7-8 June 

11-12 August 

5-6 September 

4-5 October 

2-3 November 

1-2 December 

To discuss your needs, for more information and to register, 

please contact EnginSoft Nordic, info@enginsoft.se 

UK 

The workshops are designed to give delegates a good appreciation 

of the functionality, application and benefits of 

modeFRONTIER. The workshops include an informal blend of 

presentation plus ‘hands-on’ examples with the objective of 

enabling delegates to be confident to evaluate 

modeFRONTIER for their applications using a trial license at 

no cost. 

modeFRONTIER Workshops 

Warwick Digital Laboratory, Warwick University 

12 April 

12 May 

21 June 

20 July 

17 August 

14 September 

13 October 

22 November 

14 December 

modeFRONTIER Workshops with InfoWorks CS Warwick Digital 

Laboratory 

26 May 

9 November 

Please register for free on www.enginsoft-uk.com 

Multi-Disciplinary Optimization Training Course. 

International Digital Lab, Warwick University 

16-17 May 

6-7 September 


For more information and to register, please visit www.enginsoft-uk.com. 

Contact: Bipin Patel, info@enginsoft.com 

24-25 May - National Manufacturing Debate 2011. Vincent 

Building (Building 52), Cranfield campus, Cranfield 

University EnginSoft will be attending. 

www.cranfield.ac.uk/sas/manufacturingdebate 

27-29 April - ESAFORM 2011. 14th International ESAFORM 

Conference on Material Forming. Belfast, Northern 

Ireland/UK. The purpose of this conference is to facilitate 

the communication between specialists in various fields of 

material forming sciences. Presentations concerning all the 

steps of material forming processes are welcome: from fundamental 

studies to applied aspects, from experimental to numerical 

research. Gino Duffett of AperioTec has been invited 

to give a plenary on advances and the future of simulation in 

the manufacturing industry 

www.qub.ac.uk/sites/ESAFORM2011/ 

GREECE 

9 May - 5th PhilonNet CAE Conference. Athens. If you would 

like to present your work with ANSYS (including CFX, Fluent 

and Ansoft products), ANYBODY, DIFFPACK, ESACOMP, eta/DY- 

NAFORM, eta/VPG, Flowmaster, FTI, LS-DYNA, 

modeFRONTIER, MOLDFLOW, SIMPLEWARE or ADVANTEDGE 

please send your abstract to: info@philonnet.gr 

For more information, please visit: www.philonnet.gr 

USA 

Courses on Design Optimization with modeFRONTIER 

Sunnyvale, CA 

For more information, please contact: training@ozeninc.com 

www.ozeninc.com 

JAPAN 

17 June - CDAJ CAE Solution Conference 2011 

modeFRONTIER Conference Day 

PAN PACIFIC Yokohama Bay Hotel Tokyu 

http://www.cdaj.co.jp/ 

Europe, various locations 

modeFRONTIER Academic Training 

Please note: These Courses are for Academic users only. The 

Courses provide Academic Specialists with the fastest route 

to being fully proficient and productive in the use of 

modeFRONTIER for their research activities. The courses combine 

modeFRONTIER Fundamentals and Advanced 

Optimization Techniques. 

For more information, please contact Rita Podzuna, 


To meet with EnginSoft at any of the above events, please 

contact us: info@enginsoft.com

ENGINSOFT INTERNATIONAL 

ANSYS ITALIAN 

CONFERENCE 2011 CONFERENCE 2011 

CAE TECHNOLOGIES FOR INDUSTRY 

® 

20-21 OCTOBER 

CALL FOR PAPERS 

IS NOW OPEN 

VERONA -IT 

www.caeconference.com 

Two major events coming together for the most significant occasion in the Italian CAE Calendar

ICEPAK 13.0: buone notizie per i progettisti elettronici ... - EnginSoft

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