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APPLIED COMPUTATIONAL FLUID DYNAMICS VOL XV ISSUE 1 • SPRING 2006 

ACADEMIC 

A Dry Passage 

to the Afterlife 

AUTOMOTIVE 

Citroën CS in 

a Crosswind 

Flexible 

Fliers 

ENVIRONMENTAL 

Activated Sludge 

Basins Get on Track 

FOOD 

Looking Inside 

Dough Mixers 

PROCESS INDUSTRIES 

SUPPLEMENT INSIDE!

EDITOR’S NOTE 

THE TERM “MULTIPHYSICS” has been a part of 

simulation engineers’ vocabulary for some time. It 

describes any situation where two or more physical 

phenomena are coupled together. Often, the term is 

used to describe simulations that involve both fluid 

and structural mechanics, such as fluid-structure interaction 

(FSI). During the past few years, we have run 

stories on FSI in Fluent News and in the current issue, 

several stories are featured that illustrate different 

approaches to this difficult engineering problem. The 

articles cover applications ranging from aerospace 

(p. 5) to healthcare (p. 10). Most describe tightly 

coupled interactions between CFD and structural 

solvers and one illustrates the use of MpCCI (Meshbased 

parallel Code Coupling Interface, from 

Fraunhofer SCAI) to manage the coupled calculation 

(p. 11). Not all such applications are tightly coupled 

however, and the Support Corner (p. 34) describes a 

tool, based on user-defined functions in FLUENT, that 

can be used to transfer data between fluid and structural 

solvers for loosely coupled interactions. 

In addition to the FSI stories, several of the articles in 

the Process Industries supplement could be given the 

multiphysics label as well. Multiphase flows and reacting 

flows are examples where strong coupling exists 

between either separate fluid phases (p. S13 - S14) or 

chemical species through temperature-dependent 

reactions (p. S8 - S14). Traditional processes such as 

mixing are covered (p. S3 - S6) as are novel processes 

that involve the flow through microchannels (p. S7) or 

the extrusion of foam products (p. S16). 

As is customary with Fluent News, the current slate of 

articles represents engineering efforts from many corners 

of the world. Air conditioning units being manufactured 

in Malaysia (p. 13) and aircraft fuselage design 

being performed in China (p. 19) are two examples. 

There are several articles from the US and Europe, 

covering topics such as automotive components (p. 16 

- 17), a unique dough conditioner (p. 22), a car in a 

crosswind (p. 14), and a two-stroke engine (p. 18). A 

few new applications of CFD are also presented. The 

flow inside an infant incubator is optimized (p. 24), and 

a ventilation system is installed in an Egyptian tomb to 

help preserve the ancient wall paintings (p. 28). 

Students shine once again with their unbound energy 

for innovation. This time, their efforts are used to develop 

a human-powered submarine (p. 29). 

Last November, a 64-bit version of FLUENT running on 

a Windows Cluster was showcased at Supercomputing 

2005. The performance of this exciting new capability 

is summarized (p. 30). In other product news, the 

upcoming releases of GAMBIT 2.3 and TGrid 4.0 are 

reviewed (p. 32). These new products and computing 

opportunities will make our work more manageable 

in the months and years to come. Please continue 

to keep us informed of your own efforts to push the 

limits of CFD and let us know what CFD has been able 

to do for you. 

LIZ MARSHALL 

fluentnews@fluent.com 

Fluent News is published by 

10 Cavendish Court • Lebanon, NH 03766 USA 

603 643 2600 • www.fluent.com 

© 2006 Fluent Inc. All rights reserved. 

ON THE COVER: 

Contours of presssure on the wing of the 

Aermacchi M346 Advanced Trainer 

Courtesy of Politecnico di Milano and Aermacchi SpA 

ON THE SUPPLEMENT COVER: 

Contours of velocity magnitude on a plane between 

two impellers in a stirred tank and vortex structures 

near the upper impeller, colored by velocity magnitude 

Courtesy of Prague Institute of Chemical Technology 

Editor: Liz Marshall 

Assistant Editor: Susan Wheeler 

Contributing Editors: Erik Ferguson and Keith Hanna 

Design: Lufkin Graphic Designs 

FLUENT, FiDAP, GAMBIT, POLYFLOW, G/Turbo, MixSim, 

FlowLab, Icepak, Airpak, and FloWizard are trademarks of 

Fluent Inc. Icepak and Airpak are joint developments of 

Fluent Inc. and ICEM-CFD Engineering. All other products 

or name brands are trademarks of their respective holders.

CONTENTS 

16 

20 13 

17 

8 

29 S3 

5 

S5 10 

31 

FEATURES 

APPLICATIONS 

5 

FLUID-STRUCTURE 

INTERACTION 

Flexible Fliers in the 

Transonic Regime 

13 HVAC 

8 

Unconventional Sail Design 

10 

Artificial Heart Valve Takes 

Shape 

16 

11 FSI Controls Flow Rate 

Fan Research Makes 

Cool Air Conditioners 

14 AUTOMOTIVE 

Citroën C5 in a 

Crosswind 

17 

Torque Converters 

Get In Gear 

Emissions Control 

Through Carbon 

Canisters 

18 

POWER TOOLS 

Scavenging in a 

Stratified, Charged 

Two-Stroke Engine 

19 AEROSPACE 

Nacelle Impact on 

Aircraft Wing & 

Fuselage 

20 

Compressors Benefit 

from the NASA 

Rotor 37 

21 FOOD 

Image-based Meshing: 

Easy as Pie 

22 

Looking Inside Dough 

Mixers 

24 HEALTHCARE 

CFD Assists Neonatal 

Intensive Care 

26 ENVIRONMENTAL 

Activated Sludge 

Basins Get on Track 

Fluent News · Spring 2006 3

CONTENTS 

DEPARTMENTS 

28 

ACADEMIC NEWS 

A Dry Passage to the Afterlife 

29 

Student Submariners Peddle 

Their Way to Victory 

14 

11 

29 

Convective Motions Inside a 

Gearbox 

30 

PRODUCT NEWS 

Fluent & Microsoft Team to 

Deliver 64-bit FLUENT on 

Windows Clusters 

31 

Quick Turnaround with 

Rapid Flow Modeling 

32 

Impressive Preprocessing 

33 PARTNERSHIPS 

Discrete Element Modeling 

of Particles for FLUENT 

34 

SUPPORT CORNER 

Mapping Thermal Data 

from FLUENT to Structural 

Codes Quickly 

23 

S7 32 

36 

AROUND FLUENT 

Fluent Opens Larger Office 

in Ann Arbor, MI 

17 

27 

36 

Upcoming User Group 

Meetings 

PROCESS INDUSTRY 

SUPPLEMENT 

S6 

s2 OVERVIEW 

Transporting CFD to 

Process Engineers 

s12 FURNACES 

Ultra-low NOx 

Burners Get Cracking 

S11 

S15 

s3 MIXING 

Tracing Homogenization 

s5 

Finding the Optimum 

Blend Time Calculation 

s7 MICROREACTORS 

Liquid Mixing 

in Microreactors 

s8 

THERMAL RUNAWAY 

Preventing Runaway 

Reaction Accidents 

s10 EMISSIONS 

Scrubbers for 

Flue Gas Cleanup 

s13 MULTIPHASE 

Controlling Droplet 

Size Distribution in 

Emulsions 

s14 Understanding 

Fluid-Bed Coating 

s15 PUMPS 

Pumping out New 

Designs More Quickly 

s16 EXTRUSION 

Polymer Processing 

Simulation for Foam 

Extrusion 

4 Fluent News · Spring 2006

FLUID-STRUCTURE INTERACTION 

Flexible Fliers 

in the Transonic Regime 

By L. Cavagna, G. Quaranta, P. Mantegazza, Department of Aerospace Engineering, Politecnico di Milano, Italy 

D. Marchetti, M. Martegani, M346 Program, Aermacchi SpA, Varese, Italy 

Pressure contours on the surface of the 

M346 Advanced Trainer 

FOR AN AIRCRAFT IN TRANSONIC FLIGHT, shock 

waves appear and move through the flow field. As a 

consequence of the unsteady flexible motion of the 

aircraft skin, shock waves dynamically modify the 

pressure distribution, eventually causing a drop in 

the flutter velocity, or speed at which the aircraft 

vibrations are no longer damped and instead grow 

in amplitude. The premature onset of flutter during 

transonic conditions, otherwise known as the “transonic 

dip” effect, is usually underpredicted by classical 

unsteady potential methods [1]. In a research 

project at the Politecnico di Milano [2], a computational 

aeroelastic (CA) model for deformable aircrafts 

in the transonic regime has been developed. Special 

care has been taken to create an environment that 

integrates all of the relevant physics, keeping in 

mind the high number of analyses that typically have 

to be run during the development of an aircraft. The 

implemented methods have been applied to the 

new generation Aermacchi M346 trainer. 

The aeroelastic solver core is implemented in FLUENT 

through the Scheme programming language. 

Access to flow variables and data manipulation is 

permitted through an extensive use of user-defined 

functions (UDFs) called at different times by the 

master Scheme library. The structural model comes 

from the commercial finite-element code MSC- 

Nastran, which has a long tradition in the aerospace 

industry. 

The adoption of a partitioned approach for the solution 

of fluid-structure interaction (FSI) problems 

requires the definition of an interface method to 

The Advanced Trainer M346 developed by Aermacchi 



The aeroelastic procedure is illustrated for geometry elements (top) and the 

resulting mesh (bottom) for a case of amplified motion 

The AGARD 445.6 wing showing two modes of 

structural deformation 

exchange displacements and velocities from the 

structural grid to the aerodynamic wet surfaces of 

the CFD grid and to transfer back aerodynamic 

forces on the structural nodes. The structural and 

CFD models are described in a very different and 

often not compatible way; this is especially true in 

an industrial environment, where the models usually 

come from different departments. For the CA 

model developed at Politecnico di Milano, an interfacing 

procedure based on a “mesh-free” moving 

least squares (MLS) method [3] is used. This method 

is suitable for the treatment of geometrically complex 

configurations and, unlike many other interface 

methods, it ensures the conservation of momentum 

and energy transferred between the fluid and structure, 

a key factor for stability analysis like flutter. In 

fact, the comparison of spurious energy created or 

destroyed by the interface scheme may alter the 

stability boundary of the system. 

The primary assessment necessary for the aeroelastic 

certification of an aircraft is related to analyses of the 

linearized solutions to catch instabilities and specifically, 

flutter conditions. In the case of strong nonlinearities 

in the flow field, it is necessary to assess the 

stability of each movement associated with each 

equilibrium point of the aeroelastic system. 

Consequently, any flight configuration could potentially 

assume a different stability behavior. However, if 

there are no abrupt changes in the fluid flow, it is 

reasonable to consider the linearization around a specific 

flight condition as representative of the behavior 

at nearby flight points, which are characterized by 

small differences in the mass and stiffness distributions, 

and consequently small variations in the aircraft 

attitude. In the work presented by Melville [4] on an 

application similar to the one considered here, a 

stability investigation was conducted following a 

“numerical experiment” technique in which the 

stability is determined by analyzing the decaying or 

diverging behavior of time responses. This method is 

extremely time-consuming because a large number 

of analyses are required to bracket the flutter conditions. 

An approach based on the adoption of a CFD 

solver for generating reduced order models (ROMs) of 

transonic aerodynamics is more affordable and 

industrially oriented. As in classical flutter analyses, 

the result from this type of approach is a linearized 

model in either the frequency or time domain that 

can be used as an efficient tool for stability boundary 

assessment and dynamic loading response analysis. 

In order to avoid any possible source of error, a 

backup procedure should also be available to run 

the coupled nonlinear analysis. This alternative 

procedure can be used to verify and validate key 

instability points obtained by using the simplified 

linearized approach. A direct time integration can 

be easily implemented for this purpose using a 

loosely coupled, partitioned algorithm. 



To correctly represent the structural deformation of 

the aircraft, the CFD computational grid must be 

modified at each time step in order to be compatible 

with the new aircraft shape. Grid deformation 

has a significant impact on the time required by 

CFD for aeroelastic simulations. For the sake of overall 

computational efficiency, nonlinear models for 

grid deformation have been avoided. Instead, an 

elastic analogy is used to represent the grid as a linear 

elastic continuum with a local Young’s modulus 

proportional to the minimal dimension of each element. 

The structural analogy provides ample freedom 

for choosing the material constitutive properties 

that will rule the grid deformation. Furthermore, 

by dividing the computational domain into different 

fluid zones, the deformation process can be customized 

by choosing different structural properties 

for the adjacent wall zones. The result is a flexible 

and extremely robust grid deformation algorithm. 

The structural model is represented as a set of 

modal shapes. To accelerate the solution time, the 

linearity of the problem is exploited, allowing the 

CFD grid deformation to be expressed as a superposition 

of grid deformations computed for each 

modal shape. All of the deformed grids associated 

with each modal shape are computed only once 

and stored in a database. Special care must be 

taken in the treatment of the governing surface 

movements since surface rotation can modify the 

CFD domain topology by creating cuts and new 

wall surfaces at the boundaries. As a result, the correct 

treatment of these conditions ideally requires 

the creation of a new mesh during the transient 

simulation, significantly increasing the computational 

burden. To efficiently overcome this hurdle, a 

solution based on a non-conformal mesh topology 

is used. This allows distinct cell domains to be 

defined that are associated with the movable surfaces 

that may be deformed independently from 

the rest of the grid. The implemented solver 

has been applied to the AGARD 445.6 wing, a 

classic aeroelastic benchmark case with published 

data [5]. The results are in good agreement with 

measurements in the transonic regime and correctly 

predict the transonic dip. 

References: 

1 Isogai, K.: On the Transonic-dip Mechanism of Flutter 

of a Sweptback Wing. AIAA Journal, 17:735-795, 1979. 

2 Cavagna, L., Quaranta, G., Ghiringhelli, G.L. and 

Mantegazza, P.: Efficient Application of CFD Aeroelastic 

Methods Using Commercial Software. In International 

Forum on Aeroelasticity and Structural Dynamics 

IFASD-2005, Munich, Germany, June 28 - July 1 2005. 

3 Quaranta, G., Masarati, P. and Mantegazza, P.: A 

Conservative Mesh-free Approach for Fluid-structure 

Interface Problems. Papadrakakis, M., Oñate, E. and 

Schrefler, B., Editors, in International Conference on 

Computational Methods for Coupled Problems in Science 

and Engineering, CIMNE, Santorini, Greece, 2005. 

4 Melville, R.: Nonlinear Mechanisms of Aeroelastic 

Instability for the F-16. AIAA Paper 2002-0871, January 

2002. 

5 Yates, E.C.: AGARD Standard Aeroelastic Configurations 

for Dynamic Response. I wing 445.6. R 765, AGARD, 

1985. 

V F 

Modal amplitude 

Modal amplitude 

0.6 

0.5 

0.4 

0.3 

Experiment 

FLUENT 

0.2 

0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 

Mach number 

The transonic dip, or decrease in flutter velocity index 

in the transonic regime, is nicely captured by the 

computational aeroelastic model 

.002 

.001 

.000 

-.001 

Mode 1 

Mode 2 

-.002 

0 8 16 24 32 40 48 

Structural dimensionless time 

.15 

.10 

.05 

.0 

-.05 

-.10 

Mode 1 

-.15 

Mode 2 

-.20 

0 4 8 

12 16 

Structural dimensionless time 

Modal amplitude histories for the AGARD 445.6 

wing for the case with Mach number 0.678 and 

flutter velocity index 0.34 (top) and 0.5 (bottom) 

Different fluid domains and a non-conformal mesh are used to control the motion of the various surfaces 



Unconventional 

Sail Design 

By Paolo Conti, Marco Argento, Matteo Scarponi, Dipartimento Ingegneria Industriale, Università degli Studi di Perugia, Italy 

Tornado class catamaran 

Photo Courtesy of HoldenSailing 

IN THE WORLD OF DINGHY AND YACHT 

racing, the sail design process is largely 

based on the experienced eye of the sailmaker; 

with few exceptions, the refinement 

of a given sail shape is a time-consuming 

process and requires a great amount of fullscale 

tests at sea. One of the most relevant 

issues in sail design is the correlation of a 

nominal sail shape with its wind-loaded 

shape and its actual performance. The aim 

of the present work is to provide a partial 

response to such a problem through the 

development of a systematic procedure for 

analysis and comparison of different sail 

designs. In particular, the goals are to 

estimate how given design factors affect 

the sail’s performance and to provide an 

analytical model of the phenomenon. The 

method used is based on coupled structural 

and CFD simulations and the Design of 

Experiments (DOE) technique. 

According to Richter et al. [1], computational 

fluid dynamics “has demonstrated 

the ability to predict sail and appendage 

forces under upwind conditions.” In their 

work, the authors integrated FLUENT and 

a proprietary software for structural analysis 

to estimate the flying shape of sails. 

Thanks to CFD, an adequate degree of 

accuracy can also be obtained when predicting 

the behavior of a yacht sailing 

downwind [2]. 

Regression techniques can be regarded as 

well-established tools in the naval architecture 

domain [3]. Furthermore, DOE has 

proved to be a valuable tool in sail design 

[4]. However, the potential of DOE techniques 

has not been fully explored. DOE 

offers three advantages [5]. First, a number 

of parameters (both discrete and continuous) 

and responses can be involved. 

Second, regression models can be provided 

with reasonably low computational effort. 

Third, possible design trends can be identified 

by means of appropriate representation 

of responses, or response surfaces, over a 

given design space. 

The selected case study is the design 

of asymmetrical spinnakers used by the 

Tornado, the Olympic class catamaran. A 

set of parameters representing a full 3D 

spinnaker shape has been identified and 

their domains evaluated, in order to perform 

systematic perturbations on a given 

sail design and set up a DOE-driven analysis. 

The surface mesh on the sail and wind 

tunnel boundaries 

A mesh sizing function whose source is 

the sail 



Streamlines coloured by velocity magnitude; the 

spinnaker is viewed from the leeward side 

Comparison between a design shape (left) (input to the iterative process) and the corresponding flying shape 

(right) after convergence is reached following four fluid-structure iterations 

Although such parameters refer to a sail section at a 

given mast height, their perturbations have been 

propagated to the rest of the sail in order to obtain 

results consistent with common sailmaking practices. 

The selected process factors are the sail section 

camber, its position over the chord, and the 

section twist angle with respect to the sail foot 

chord (or bottom edge of the sail). 

Because of the need to sample a significant region 

of the design space, the various designs evaluated 

are considerably different from each other; notable 

differences have been observed in terms of the sail 

angle of attack at given reference heights. The 

structural behavior of the design shapes can’t be 

represented as that of rigid bodies. In fact, the 

overall sail shape undergoes large displacements 

and a fluid-structure interaction approach 

becomes necessary. 

A “numerical wind tunnel” consisting of a coupled, 

iterative procedure involving structural and fluiddynamic 

calculations has been developed to estimate 

the wind-loaded shapes of given sails and their 

performance in terms of driving force, side force, 

and heeling moment. At each iteration, FLUENT 

evaluates the pressure field over a given sail surface 

and provides it to a structural module that yields a 

new loaded shape. For the purposes of the structural 

analysis, the sail is modeled as an infinitely flexible 

web with fixed elongation [6]. Each structural 

step consists of a constrained optimization routine 

whose objective function is the work of aerodynamic 

forces acting on the sail. 

Since a wind tunnel facility suitable for the testing of 

model sails is available at the University of Perugia, 

the computational domain for the CFD analysis has 

been scaled accordingly, for validation purposes. 

The numerical wind tunnel underwent preliminary 

tests showing good agreement with actual flow field 

data. In order to be consistent with the actual 

process of sail trim, the ideal flow incidence with 

respect to a given reference section of the sail 

should be preserved until the iterative method converges. 

As an example, critical conditions may occur 

at low angles of attack, where the leading edge of 

the sail collapses easily. 

A proper module was therefore implemented 

through GAMBIT scripting language, which allowed 

the sail to be correctly positioned with respect to 

the flow field at the beginning of each FLUENT run, 

simulating actions taken by the helmsman. The 

problem just described implies that a unique meshing 

strategy must be suitable for several sail shapes 

(those generated at each step of the iterative 

process). The use of sizing functions allowed these 

difficulties to be overcome and, at the same time, 

provided a satisfactory mesh quality independent of 

the sail shape considered. 

Results of all simulations were collected and 

processed through an analysis of variance tools, in 

order to select the geometric parameters affecting 

sail performance. Regression models and response 

surfaces were then evaluated, in order to provide 

guidelines for future improvements of a given 

design. A validation of the present approach has 

been carried out by means of wind tunnel tests and 

consists of two stages. First, the performance in 

terms of driving force coefficient was evaluated. 

Second, the predicted and actual flying shapes of a 

given sail were compared. An optical measurement 

system [7] allowed the wind loaded shapes of 

model sails during wind tunnel runs to be digitized. 

In both cases, reasonable agreement with numerical 

results was found, which suggests the possibility 

of using the methodology described above as a 

valuable tool for sail designers. 

Response surface showing the influence of camber 

and position, while twist is fixed to its mid-range 

value, obtained from the final regression model 

References 

1 Richter H.J., Horrigan K.C. and Braun J.C.: 

Computational Fluid Dynamics for Downwind Sails. In 

Proc. of The 16th Chesapeake Sailing Yacht 

Symposium, 2003. 

2 Jones, P. and Korpus, R.: International America’s Cup 

Class Yacht Design Using Viscous Flow CFD. In Proc. of 

The 15th Chesapeake Sailing Yacht Symposium, pp. 

27-34, 2001. 

3 Battistin, D., Peri, D., and Campana, E.F.: Geometry 

and Resistance of the IACC Systematic Series Il Moro di 

Venezia. In Proc. of The 17th Chesapeake Sailing Yacht 

Symposium, pp. 33-51, 2005. 

4 Lasher, W.C., Sonnenmeier, J.R., Forsman, D.R., Zhang, 

C. and White, K.: Experimental Force Coefficients for a 

Parametric Series of Spinnakers. In Proc. of The 16th 

Chesapeake Sailing Yacht Symposium, 2003. 

5 Myers, R.H. and Montgomery, D.C.: Response Surface 

Methodology. John Wiley & Sons, New York, 2002. 

6 Le Maitre, O., Huberson, S.G. and Souza de Cursi, J.E.: 

Application of a Non-convex Model of Fabric 

Deformations to Sail Cut Analysis. J. Wind Engineering 

and Industrial Aerodynamics, Vol. 63, pp. 77-93, 1997. 

7 Barone, S., Bianconi, F., Conti, P., Razionale, A. and 

Scarponi, M.: Acquisition and Modelling of 3D Sail 

Shapes. CAD 2004, International CAD Conference and 

Exhibition, Pattaya Beach, Thailand, 2004. 

more.info@ 

matteo.scarponi@unipg.it 



t = 1.00 sec 

Artificial 

Heart Valve 

Takes Shape 

t = 1.04 sec 

t = 1.14 sec 

By Jan Vierendeels, Joris Degroote, Kris Dumont, Lieve Lanoye, Pascal Verdonck, 

Ghent University, Belgium 

AT GHENT UNIVERSITY, BELGIUM, research on fluid-structure interaction 

(FSI) modeling has been ongoing since 1996. At the beginning, in-house codes 

were developed to compute the interaction between the motion of the cardiac 

wall and the blood flow in the heart [1]. During the past ten years, CFD and CSD 

(computational structural dynamics) packages have made significant progress 

and nowadays, the best approach for coupling fluid motion and structural displacement 

is to use existing software packages as partitioned solvers. When using 

a partitioned approach, however, it can be difficult to obtain convergence, especially 

when the interaction between the different motions is strong. This is the 

case for blood flow in the heart and for blood flow through a heart valve with 

flexible leaflets. Both of these types of blood flow have been simulated using the 

FSI approach developed at Ghent University. The blood flow in the moving 

geometries is computed with FLUENT 6.2, and the structural problem is solved 

using in-house software. The coupled calculation is robust and does not cause 

undue convergence difficulty, even for strongly interacting system responses. 

During the calculation, an implicit coupling method is used. For each new time, 

a new position of the boundary and the corresponding load distribution on that 

boundary is sought. This information is the result of simultaneous solutions of the 

CFD and CSD solvers, and could be obtained by alternating the solver calls. For 

strongly coupled cases however, such an approach can only be stabilized by 

reducing the underrelaxation factors or by using an Aitken-like technique, both 

of which tend to slow the rate of convergence. 

t = 1.36 sec 

t = 2.00 sec 

The figures show a flexible leaflet being opened by 

an accelerating flow; the stiffness of the leaflet is very 

small, so that it undergoes a huge displacement from 

the closed to the fully open position and back again 

To speed the convergence, a reduced order model is used [2]. If the Jacobian of 

the fluid solver were known when solving the structural problem, one would 

know how the load would change when a certain boundary displacement is 

applied, so the load change would not need to be calculated with the fluid solver. 

The boundary condition for the structural solver (the load) would not need to be 

assumed constant during this call, but could be written as a function of the 

unknown boundary position itself, resulting in a much better prediction for the 

boundary position. Since the Jacobian of the fluid solver is not available, a 

reduced order model of the solver can be built for which a Jacobian can be 

constructed. This approximation of the Jacobian of the fluid solver is then used 

for the coupled calculation. During each time step, whenever FLUENT is called 

a boundary displacement mode is applied and the corresponding load change 

at the boundary is retrieved through user defined functions (UDFs). This 

information is used to build up the reduced order model for the fluid problem. 

The structural solver is then called with the variable load boundary condition. 

FLUENT is called again and the reduced order model is updated. The loop is 

executed until a residual drop of four to five orders of magnitude is obtained. 

This new technique for FSI problems has proved to be robust and powerful, and 

is ideally suited for FSI problems with strong coupling. 

References: 

1 Vierendeels, J.A., Riemslagh, K., Dick, E. and Verdonck, P.R.: Computer Simulation of 

Intraventricular Flow and Pressure Gradients During Diastole. Journal of Biomechanical 

Engineering – Transactions of the ASME, 122(6):667-674, 2000. 

2 Vierendeels, J.A.: Implicit Coupling of Partitioned Fluid-structure Interaction Solvers using 

a Reduced Order Model. In Proc. of the 35th AIAA Fluid Dynamics Conference and 

Exhibit, June 6-9 2005, Toronto, Ontario, Canada. AIAA-2005-5135. AIAA Meeting 

papers on disc, vol. 10, nr 11-12. ISBN 1-56347-763-7. 



FSI Controls 

Flow Rate 

By Subham Sett, ABAQUS, Inc., Providence, Rhode Island, USA 

FLOW CONTROL DEVICES are commonly employed in 

the automotive, biomedical, and consumer appliances 

industries to maintain a constant bulk flow rate for varying 

inlet pressures. Fluid pressure variations may result 

from pipe friction loss, downstream restrictions, distance 

from the water tower, or elevation of the water tap, for 

example. Minimizing the impact of inlet pressure variation 

on the flow is essential for the proper performance of 

fluid systems. 

A schematic cross 

section of a typical 

VernaFlo device 

before (top) and 

after (bottom) the 

rubber component 

is deformed 

Vernay VernaFlo ® flow controls are custom-designed flow 

management devices used in a wide range of applications 

where consistent, reliable fluid flow is essential. This study 

evaluates the performance of a custom VernaFlo device, 

using a fully coupled fluid-structure interaction (FSI) 

analysis with ABAQUS and FLUENT. The computational 

results compare favorably with available experimental 

data. 

In a typical VernaFlo device, an elastomeric rubber component 

is housed inside the flow path. This rubber insert 

rests on a rigid seat and deforms under the influence of 

the incoming flow. At low operating pressures the rubber 

component undergoes very little deformation and allows 

flow 

The CFD sub-domain where the fluid flows (red) and the ABAQUS sub-domain 

which, for the VernaFlo valve, is a rubber insert that deforms for different flow rates 

(white) 

Static flow pressure (top) and Mises stress (bottom) at 20, 40, 60, and 90 psi; at high pressures, the rubber insert moves closer to the axis, 

stabilizing the flow rate 



2.5 

Flow rate (L/min) 

2.0 

1.5 

1.0 

0.5 ABAQUS-FLUENT 

Experiment 

0.0 

0 20 40 60 80 100 

Inlet pressure (psi) 

120 

Experimental validation of the computation results 

the flow to develop. With increasing upstream pressure, the deformation increases, restricting 

the orifice diameter and thus limiting the fluid flow. Capturing the interaction between 

the fluid flow and the structural deformation is critical to accurately predicting the shape of 

the rubber component and the subsequent flow behavior. 

The FSI analysis of the valve with ABAQUS and FLUENT makes use of a co-simulation technique 

managed by MpCCI (Mesh-based parallel Code Coupling Interface). The fluid and 

structural domains are modeled and solved separately, and solution information is 

exchanged at the fluid-structure interface. The ABAQUS sub-domain includes the rubber 

component, which is modeled using reduced order hybrid axisymmetric elements. The 

rubber component presses against a rigid seat. Penalty contact with a friction coefficient of 

0.5 is defined between the rubber and the rigid seat. The simulation includes the effect 

of geometrical and material nonlinearities. The CFD sub-domain models the flow path, 

including a short upstream section, the section around the rubber component, and a long 

downstream section. The upstream variation in pressure is accounted for using a pressureinlet 

boundary condition, and a zero gauge pressure is used at the outlet. To enable local 

remeshing, the flow path is modeled using triangular elements. The water is modeled as a 

turbulent, incompressible fluid, and the cavitation model is enabled. 

Velocity magnitude contours on a 2D model of the 

cross flow fan; the flow is from bottom to top 

The fluid-structure interface is defined using MpCCI. During the simulation, the pressures 

acting on the rubber component in the fluid sub-domain are mapped and transferred to the 

structural sub-domain in ABAQUS via MpCCI. ABAQUS computes the deformations and the 

resulting stress state in the structure. The interface deformation quantities are then mapped 

and transferred from the structural sub-domain to the fluid sub-domain in FLUENT via MpCCI. 

This process of exchanging solution quantities continues incrementally until the analysis is 

complete. 

The analysis results identify how variations in inlet pressure affect the bulk fluid flow rate 

through the device, and how the fluid flow affects the deformation of the rubber component. 

Contour plots of the fluid pressures at inlet pressure levels of 20, 40, 60 and 90 psi and 

the corresponding deformed shapes show that with an increase in the inlet pressure, the rubber 

component deforms and undergoes increased contact with the rigid seat. Partial contact 

is observed at 20 psi, and full contact is established at 60 psi. The constriction path narrows 

during the inlet pressure rise, resulting in an increased resistance to the fluid flow and a 

region of very high stresses. At a given deformation state, the increased material stiffness 

helps maintain the bulk flow rate at a fairly constant level. As the flow quickens through the 

narrow constriction region, the fluid pressure drops significantly, resulting in a dramatic drop 

in the absolute pressure of the liquid. Cavitation occurs at higher upstream pressures. 

Contours of stream function for the cross flow fan 

The computational flow rate results were found to be in good agreement with experimental 

data. The flow rate increases from 0 to 2.1 liters/min during the initial pressure ramp-up from 

0 to 20 psi and has nearly constant flow in the operating pressure range of 20 to 120 psi. 

Acknowledgments 

ABAQUS, Inc. would like to thank Jim Bailey at Vernay Labs for providing the model and experimental 

data for the Vernay VernaFlo device, and David Schowalter at Fluent Inc. for assisting with the cavitation 

modeling. 

Velocity vector detail shows recirculation regions in 

one area of the cross flow fan 


HVAC 

Fan Research Makes 

Cool Air Conditioners 

By Teng-Kiat Lim Ph.D. and Chee-Onn Chan, Department of Research and Application, O.Y.L. R&D Center, Selangor, Malaysia 

SIGNIFICANT TECHNICAL CHALLENGES face 

companies in the HVAC industry today. In an 

increasingly competitive global market, HVAC systems 

need to make use of advanced technologies 

and improved component performance. At the 

O.Y.L. R&D Center, CFD is used for the analysis and 

optimization of fans, compressors, heat exchangers, 

and ducts, and it plays an important role in HVAC 

product lifecycle management (PLM) as well. CFD 

helps reveal flow structures and comprehensive flow 

field information where experimental techniques 

cannot provide adequate resolution. 

Consider, for example, a split-type air conditioner 

that consists of one outdoor condensing unit (housing 

an air-cooled condenser, propeller fan, and 

refrigerant compressor) and one or more indoor 

units (housing an evaporating coil and cross flow 

fan). Split-type air conditioners are popular in residential 

buildings because of their simplicity and 

flexibility. If the air flow in the outer unit is not 

properly controlled, however, poor recirculation can 

cause the coil temperature to rise and the system 

performance to suffer. A series of simulations has 

been performed to study the various components 

of this type of system, and based on the results, 

modifications have been made. 

A 3D model of a 3-bladed fan in the outdoor condensing 

unit was created and used to visualize the 

air flow near the housing and in the discharge. The 

steady multiple reference frames (MRF) model in 

FLUENT was used along with the standard k-ε 

turbulence model and standard wall functions. A 

tetrahedral mesh of about 1 million cells was generated 

using GAMBIT and TGrid. The triangular surface 

mesh was refined in the regions around the 

hub, tip, and blade edges. A uniform air velocity 

was imposed at the condenser inlet boundary, and 

a constant pressure was applied to the outlet. The 

results illustrated that when the steady incoming air 

flow enters the rotating zone of the fan, a strong 

radial component develops, driving the flow 

towards the side walls. This behavior is enhanced by 

the existence of an outlet front panel that partially 

blocks the flow. The CFD predictions were found to 

be in good agreement with air flow test measurements. 

Studies on new front panel designs are 

currently being conducted so that better air flow 

performance can be achieved. 

A two-bladed fan has also been studied. For this 

case, an unstructured mesh of about 1.2 million elements 

was needed to give a mesh-independent 

result. The 3D, double precision simulation also 

made use of the MRF and standard k-ε models. The 

results indicated that even though the front panel 

impacts the fan performance, a more uniform air 

flow distribution can be achieved, along with a 

lower pressure drop. 

The cross flow fan (CFF) operates in a fundamentally 

different way than axial or centrifugal fans. Flow 

enters across the full width of the rotating impeller 

and exits on the opposite side. The flow structure 

inside the impeller can be divided into two regions: 

an eccentric vortex or recirculation region having 

closed streamlines, and a through-flow region connecting 

the inflow and outflow sectors. The blades 

typically have a uniform thickness and a circular arc 

profile. The performance and stability of a CFF are 

governed by the geometrical parameters of the 

impeller and casing. Using the sliding mesh model, 

a transient 2D analysis was performed to study the 

effect of parameters such as blade angle, radius 

ratio, and number of blades. Maximum energy 

transfer through the impeller takes place in the 

region where the flow follows the blade curvature. 

Radial velocity is not uniform through the blade 

channels. More flow leakage is observed through 

the tongue clearance portion at low flow coefficients 

and static pressure is always negative in and 

around the impeller region 

Absolute pressure on the 3-bladed fan (top) and 

2-bladed fan (bottom) 


AUTOMOTIVE 

Citroën 

Vortex structures colored by streamwise velocity show 

the instantaneous and highly three-dimensional flow; 

note the interaction between the streamwise vortices 

formed at separation on the front fender and the 

A-pillar on the leeward side 

Postprocessed by Ensight from CEI and Distene 

AS PASSENGER VEHICLES BECOME LARGER, their stability 

and safety characteristics become more sensitive to aerodynamic 

forces. Such effects are particularly important for vehicles exposed 

to a crosswind [1]. For instance, all drivers are familiar with the 

effect of cross-wind gusts, like those that may occur when a vehicle 

emerges from a tunnel or during overtaking. CFD can provide 

detailed information for understanding the changes in car behavior, 

handling, and performance under these conditions. 

Simulations are routinely performed at PSA using engineering 

approaches that are based on the solution of the Reynolds-averaged 

Navier-Stokes (RANS) equations. While this approach is often 

adequate for steady attached flows with no recirculation regions, 

numerous studies have shown the limitations of the RANS 

approach to accurately predict flows with massive separations and 

that are fundamentally unsteady. 

Large eddy simulation (LES) can have greater success than RANS 

methods in predicting separation and large scale unsteady flows, 

particularly in the wake and on the leeward side of vehicles under 

crosswind conditions. In recent years, this type of unsteady 

calculation has been increasingly used in industry and FLUENT has 

made a special effort to develop industrial-strength validation 

examples for LES modeling techniques [2,3]. 

PSA Peugeot Citroën has developed a strong expertise in CFD for 

external aerodynamics, cabin modeling, underhood thermal management, 

and more. In-house methodologies have been developed 

and are regularly improved, in order to fulfill new project 

requirements, which are getting more and more demanding, both 

in terms of accuracy and turn-over time. Computations are done 

in conjunction with wind tunnel experiments, either on full 

scale prototypes or, as in the case presented here, on a 1:5 scale 

model. Different measurement techniques are used, such as 

particle-image velocimitry (PIV), pressure taps, and tomography. 

The different methodologies also benefit from a collaboration 

between PSA and Fluent, and this was the basis of a recent joint 

project to address the potential benefit and cost of LES compared 

to the current RANS approach for semi-realistic configurations. 

Fujitsu/Siemens provided access to a large-scale high performance 

computing (HPC) facility for this project. 

Model Exp SST k-ω LES WALE LES WALE RSM v 2 -f 

(35M cells) (65M cells) 

Drag (SCx) 0.70 0.66 0.69 0.68 0.71 0.73 

14 Fluent News · Spring 2006 

Detailed force and torque components 

acting on the vehicle; under crosswind 

conditions, the side force (SCy) and 

yaw moment (SCn) are the most 

influential for vehicle stability 

Forces 

Moments 

Side (SCy) 2.22 2.00 2.19 2.18 2.30 2.10 

Lift (SCz) 1.40 1.66 1.27 1.30 1.82 1.77 

Yawing (SCn) -0.64 -0.60 -0.57 -0.59 -0.47 -0.47 

Rolling (SCl) -0.42 -0.36 -0.49 -0.49 -0.46 -0.41 

Pitching (SCm) 0.12 0.10 0.21 0.23 0.03 0.07

AUTOMOTIVE 

C5 in a Crosswind 

By Sylvain Lardeau, PSA Peugeot Citroën Automobile, Vélizy-Villacoublay, FRANCE, and Fabrice Mathey and Nicolas Vallée, Fluent France 

The RANS and LES simulations were performed on a 

1:5 scale simplified model of a Citroën C5. The crosswind 

effect was produced with the model placed at a 

yaw angle of 20°. The Reynolds number of the flow, 

based on the incoming velocity and car height, H, 

was 6.19 x 10 5 . The surface mesh (around 730,000 

elements) was generated with ANSA and was locally 

refined close to the edges and on the leeward faces of 

the vehicle. The boundary layers were generated with 

the preprocessor TGrid, using 5 layers of prisms. The 

remaining part of the domain consisted of tetrahedral 

cells and was generated with the preprocessor 

GAMBIT. Sizing functions were used to control the 

growth rate and the cell size of the mesh in the region 

of separation on the leeward side of the car. The 

minimum resolution required to resolve the relevant 

turbulent length scales was estimated from prior RANS 

simulations. Two grids (of 35 million and 65 million 

cells) were created to address the sensitivity of the 

results to the mesh resolution. The body was placed in 

an open channel with a cross section of 14.5H x 7H. 

The channel inlet was located 5H from the front face 

of the body, and the channel outlet was located 8.5H 

from the rear. The boundary layers were modeled with 

the Werner-Wengle wall-function approach. The average 

y + for the first cell off the wall was less than 50. 

The simulation benefited from the recent enhancements 

in numerics and sub-grid scale (SGS) modeling 

in FLUENT 6.2. The bounded central differencing 

scheme was used to prevent unphysical wiggles 

numerically introduced by the pure central differencing 

scheme. The non-iterative time advancement, or 

NITA algorithm significantly increased the speed of 

the transient calculation. The wall-adapting local 

eddy-viscosity (WALE) SGS model, which returns the 

correct wall asymptotic variation of the turbulent 

viscosity in a cost-effective manner, was used as 

well. The simulations were performed on a 96 CPU 

Fujistu cluster. For the 35M case, statistics were 

gathered during 16,000 iterations for an elapsed 

time of 70 hours. Statistics were gathered during 

6,000 iterations for the 65M case, for an elapsed 

time of 50 hours. 

The full mean aerodynamic forces and torques were 

compared with RANS methods and experimental 

data. Under crosswind conditions, the side force and 

yaw moment are the most influential for vehicle stability. 

The LES results showed very good agreement 

with the experimental data for these quantities. The 

difference between measurements and calculations 

was 2% for the side force coefficient (SCy), and was 

in the range of 10% for the yawing moment (SCn). 

The LES runs also gave consistent results for the 

other aerodynamic force components; the drag 

force (SCx) was predicted with a 1% error, for 

instance. It is noteworthy that even if some RANS 

simulations are able to predict side force and yawing 

moment with reasonable accuracy, none of the turbulence 

models could predict consistent results for 

all of the force components and moments. Surface 

pressure coefficients also confirm the differences 

between the different computational methods used. 

On the A-pillar on the leeward side, especially, the 

pressure drop is too strong with the RANS model. 

The two LES runs gave comparable results, indicating 

that reasonable mesh independence was 

reached with the 35 M grid, as far as global force 

coefficients are concerned. 

The visualization of the mean velocity streamlines 

indicates the formation of two streamwise vortices on 

the leeward side of the vehicle. The first one is formed 

on the front fender and the second is formed on the 

A-pillar and along the side window. Animation and 

instantaneous flow visualization demonstrate the 

highly three-dimensional, unsteady nature of these 

vortices. The predominant peak frequencies calculated 

from a Fourier transform of the pressure signal at 

several locations on the leeward side is equal to 

110 Hz, a value very close to the experimental peak 

frequency of 100 Hz. 

These simulations demonstrate how RANS and LES 

modeling approaches can complement each other 

to study car aerodynamics. Current RANS-based 

methodologies are able to correctly predict drag 

force components for attached flow and mild separation 

on realistic car geometries under uniform wind 

conditions. LES is a better tool for studying vehicle 

aerodynamics in crosswind conditions, since it provides 

a more accurate drag force component and 

pressure distribution along the vehicle. In addition, 

LES gave insight into the unsteady behavior of the 

flow, an important feature for both vehicle stability 

studies and aeroacoustic applications. 

References: 

1 Ryan, A. and Dominy, R.G.: The Aerodynamic Forces 

Induced on a Passenger Vehicle in Response to a 

Transient Cross-wind Gust at a Relative Incidence of 

30 Degrees. SAE Paper 980392, 1998. 

2 Kim, S.E.: Large Eddy Simulation using Unstructured 

Meshes and Dynamic Subgrid-scale Turbulence Models. 

AIAA Paper no. 2004-2548, 2004. 

3 Mathey, F. and Cokljat, D.: Zonal Multi-domain 

RANS/LES Simulation of Air Flow over the Ahmed Body. 

In: Engineering Turbulence Modelling and Experiments, 

6, pp. 647-656, Elsevier Ltd., 2005. 

0 

-0.5 

-1.0 

Cp 

-1.5 

-2.0 

Experiment 

LES 

RSM high-Re 

v 2 -f 

SST k-ω 

-2.5 

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 

X (m) 

Surface mesh of the vehicle generated with ANSA 

Surface pressure distribution on the windward side on 

the “eye plane”, z=1.250 m 


AUTOMOTIVE 

TORQUE CONVERTERS ARE FLUID-COUPLING DEVICES that 

transmit power from vehicle engines to the wheels. They are used 

in automobiles with automatic transmissions to smoothly control 

the torque supplied to the wheels at all speeds. 

An exploded view of the torque converter showing 

the pump (top), stator (middle), and turbine (bottom) 

Torque 

Converters 

Get In Gear 

By Vamshi Korivi, DaimlerChrysler, Auburn Hills, Michigan, USA 

Sandeep Sovani and Pepi Maksimovic, Fluent Inc. 

Torque converters are essentially chambers filled with transmission 

fluid, with the primary active parts being the pump (or impeller), 

stator, and turbine. The impeller blades rotate at the engine 

speed and impart angular momentum to the fluid as they force it 

radially outward. The turbine blades receive the fluid from the 

pump and turn it radially inward. The angular momentum thus 

absorbed by the turbine is passed to the transmission as torque. 

The flow exits from the turbine blade passages with little remaining 

angular momentum and encounters the stator blades, which 

accelerate the fluid back to the pump rotational speed. This action 

creates a torque on the stator, which is arrested by a non-rotating 

shaft built into the transmission housing. Torque multiplication 

results from the fact that the turbine torque is larger than that of 

the pump. 

Numerical simulations of torque converters are important because 

the complex topology and rotation of these devices make detailed 

experimental investigation virtually impossible. Simulations help 

identify optimal designs that yield improved performance and fuel 

economy. Using the multiple reference frames (MRF) model in 

FLUENT, a DaimlerChrysler torque converter has been studied. 

The MRF model allows the different parts of the torque converter 

(the pump, stator, and turbine) to be simulated using one relative 

orientation, but separate rotating frames. The model is well suited 

for torque converters, since the number of blades is chosen to 

prevent the amplification of harmonics and possible structural 

damage to the parts. This means that any single relative position 

of the blades is a good representation of the averaged behavior of 

the system as a whole. For the torque converter considered here, 

the number of blades for the pump, turbine, and the stator is 31, 

29, and 20 respectively. 

Contours of total pressure on the blade surfaces of 

the three elements; large pressure losses can be seen 

across the stator blades (in the middle) 

K-factor 

A hexahedral mesh of about 2.9 million cells was used for the initial 

calculation. A pressure gradient-based mesh adaption was 

subsequently performed to enhance the solution accuracy. Using 

transmission fluid and a pump speed of 2000 rpm, the turbulent 

flow was captured using the realizable k-ε turbulence model. 

Custom postprocessing tools were created for the automatic 

extraction of torque converter quantities of interest, such as the 

mass-averaged flow angles at the inlet and outlet of each element, 

the efficiency, and the torque imbalance, for example. 

The results illustrated several flow features. Total pressure contours 

were used to illustrate the pressure loss across the stator blades. 

Velocity vectors were used to check for separation regions. The 

k-factor, the ratio of pump torque to the square root of pump 

speed and an indicator of the torque converter capacity, was 

found to be within 2% of measured values at all speed ratios (the 

ratio of turbine speed to pump speed) studied. 

Efficiency 

FLUENT 

Experiment 

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 

Speed ratio 

Predictions of efficiency and k-factor as a function of 

speed ratio show good agreement with measured values 

0.9 

Predictions of efficiency, defined as the product of torque ratio 

and speed ratio, were found to be within 1.6% of the data at a 

speed ratio of 0.7. The torque ratio, defined as the ratio of turbine 

torque to pump torque, was found to be within 5% for speed 

ratios above 0.2. The ability to predict efficiency and torque ratios 

at high speed ratios is important for improving fuel economy. 


AUTOMOTIVE 

Emissions Control Through 

Carbon Canisters 

By Ranjit Singh, Expert Corporation, and Pepi Maksimovic, Fluent Inc. 

CARBON CANISTERS ARE DEVICES commonly 

used to control the emissions of volatile hydrocarbons. 

Hydrocarbons (HC) are hazardous to human 

health and the environment. For automobiles, HC 

emissions are produced during the filling of the fuel 

tank, and during vehicle operation. When the 

engine is off, evaporation from the vehicle fuel 

system still occurs, even at ambient temperatures 

typical of the diurnal cycle. Allowable HC emission 

limits are set by government regulations; for example, 

the LEV II (Low Emitting Vehicle-II) standard 

allows a certain amount of hydrocarbon emissions 

for a specific range of gross vehicle weight. 

Carbon canisters are part of the evaporative emission 

control system, which includes the fuel tank, 

vent and purge valves, and fuel lines. The role of the 

carbon canister is to store the fuel vapor generated 

in the system instead of having it escape into the 

atmosphere. The HCs are then burned off by purging 

the canister into the intake manifold when the 

engine is running. An optimum design includes a 

high working capacity, minimal pressure drop in the 

evaporative emissions system, space and size restrictions, 

and the ability to meet the mandated emission 

standards. Furthermore, the carbon utilization 

in the canister should be uniform; when less carbon 

material is needed, savings are realized. In today’s 

competitive market, carbon canister manufacturers 

must be able to predict canister performance without 

having to build and test various prototypes. 

desorbing), until breakthrough is achieved, i.e. 

when a set cumulative amount of butane passes 

through the canister outlet. Arrhenius reaction rates 

for the adsorption and desorption reactions were 

prescribed. The carbon pellets were modeled as 

a porous region. A mesh of 184,000 hexahedral 

elements was used for the laminar simulation. 

The CFD results were used to assess the design. 

Using pathlines, a region with little or no flow was 

identified in the first chamber. Ideally, the canister 

design should provide uniform utilization of the 

carbon media in all of the chambers, and this result 

suggests that it does not. Mass fraction contours 

were used to illustrate the distribution of butane at 

several times during the canister loading process. 

The growth of butane inside the canister is the 

result of the ongoing surface reactions with the carbon 

pellets, so this provides another measure of 

how well the unit is operating as a whole. A plot of 

the butane mass fraction at the outlet as a function 

of loading time can also be used to indicate the 

time when breakthrough is achieved. The carbon 

canister capacity is computed by integrating under 

this curve. 

The geometry of the carbon canister, showing the 

inflow port (blue), 3 chambers packed with carbon 

pellets (gray) and vent port (yellow) 

Pathlines show a dead zone in the upper left corner 

in the first carbon-filled chamber; the presence of 

dead zones points to underutilization of carbon 

One carbon canister design from Expert Corp. has 

been simulated using FLUENT. The purpose of the 

CFD simulation was twofold: first, to compute the 

canister capacity and pressure drop at 60 l/min of 

air flow, and second, to predict canister breakthrough 

for a gaseous mixture of butane and nitrogen 

entering the canister at 15 g/hr. The mixture 

reacts with packed carbon pellets (adsorbing and 

Contours of butane mass fraction on a slice through the canister during loading (left) and once the canister has 

reached capacity (right) 


POWER TOOLS 

Scavenging in a Stratified, Charged 

Two-Stroke Engine 

By Wolfgang Emmerich, SOLO Kleinmotoren GmbH, Sindelfingen, Germany 

TWO-STROKE GAS ENGINES are commonly used in hand-held machines, 

such as chain saws and grass trimmers. These engines have a tendency to cause 

pollution as a result of unburned hydrocarbons being released along with the 

exhaust. At SOLO Kleinmotoren, a new by-pass system has been developed that 

allows the combustion chamber to be flushed with either a lean fuel mixture or 

pure air early in the engine cycle. Fuel is injected as a rich mixture later in the 

cycle when the outlet port is closed. The by-pass system prevents the fuel from 

escaping from the combustion chamber before combustion is complete. 

A numerical investigation of the by-pass system has been carried out using the 

dynamic mesh model in FLUENT. Throughout several engine cycles, the mass 

fractions of pure air, a rich fuel mixture, and a mixture of exhaust gases were 

tracked. Combustion was approximated by resetting the pressure, temperature, 

and components of the three gaseous species each time the piston was in the top 

dead center (TDC) position. 

The operation of the engine is illustrated at right. During the suction phase, clean 

air is drawn into the crank case (blue arrow) and a fresh mixture of fuel and air 

is drawn into the by-pass channel (green arrow). As the crank case compresses, 

the clean air enters the combustion chamber through transfer ports, while the 

fuel mixture is pressed into the chamber through an injection port. Due to fluid 

dynamic constraints dictated by the local flow, the fresh air in the chamber prevents 

the fuel mixture from leaving the combustion chamber directly through 

the outlet port. Only exhaust gases (red) and pure air can be released into the 

environment. 

A schematic of the engine with the by-pass system: pure air (blue) and fuel (green) 

enter through ports on the left; following combustion, pure air and exhaust gases 

leave through the outlet port on the right 

The distributions for the three gaseous species at three different piston positions 

illustrate the success of the by-pass system. The blue surface corresponds to fresh 

air, the green surface to the fuel mixture, and the red surface to the exhaust gas 

mixture. At bottom dead center (BDC), the positions of the surfaces indicate that 

the fresh air forms a fluid shield between the fuel mixture and the outlet port. 

When the piston has advanced 50° beyond BDC, the fluid shield is not perfect, 

so a small portion of the fuel mixture can leak past the shield near the cylinder 

walls. When the piston has advanced 100° after BDC, the outlet port is closed by 

the rising piston, just before the fuel mixture has a chance to escape. 

The results of the simulation show the ability of the new engine to reduce environmental 

pollution by limiting the release of unburned hydrocarbons. The 

results also suggest that there is potential to improve the fluid dynamics of the 

fuel injection as well as the flushing of the combustion chamber to further 

enhance the performance of the new by-pass system. 

Surfaces of fresh air (blue), fuel (green), and exhaust (red) at three piston positions: 

a) bottom dead center (BDC), where the air forms a fluid shield to block the fuel, 

b) 50° beyond BDC, where some fuel leaks past the air shield near the cylinder wall, 

and c) 100° beyond BDC, at which time the rising piston has closed the outlet port 


F L U E N T N E W S S U P P L E M E N T 

Focus on CFD 

For the Process Industries 

s2 OVERVIEW 

Transporting CFD to 

Process Engineers 

s3 MIXING 

Tracing Homogenization 

s5 



s7 MICROREACTORS 

Liquid Mixing 


s8 


Preventing Runaway 

Reaction Accidents 

s10 EMISSIONS 

Scrubbers for 

Flue Gas Cleanup 

s12 FURNACES 

Ultra-low NOx Burners 

Get Cracking 

s13 MULTIPHASE 

Controlling Droplet Size 

Distribution in Emulsions 

s14 Understanding 

Fluid-Bed Coating 

s15 PUMPS 

Pumping out New 

Designs More Quickly 

s16 EXTRUSION 


Simulation for Foam 

Extrusion

OVERVIEW 


Transporting CFD 

to Process Engineers 

By Ahmad Haidari, Chemicals and Process Industry Segment Manager, Fluent Inc. 

CHEMICAL AND OTHER PROCESS INDUSTRY 

companies are driving issues such as green engineering, 

energy reduction, chemical sustainability and corporate 

profitability through innovation and the use of ingenious 

engineering and technology. Simulation software plays a 

key role in helping engineers to better understand 

processes and is assisting in productivity and efficiency 

gains across the industry. CFD in particular is gaining 

broad traction with researchers, process engineers, and 

equipment designers to help analyze and design the flow 

and performance of process equipment such as stirred 

tanks, fluidized bed reactors, separators, combustion 

systems, heat exchangers, and polymer and material 

processing and handling equipment. 

formulation accounts for phenomena such as nucleation, 

growth, dispersion, dissolution, aggregation, and 

breakage, so it is possible to describe and track changes 

in the particle population. Applications of this technology 

span a wide range of multiphase systems such as 

solid-liquid dispersions, crystallization, precipitative reactions, 

gas-sparged reactors, liquid-liquid dispersions, and 

liquid-liquid separation equipment. Second, for granular 

mixtures, the macroscopic particle model (MPM), available 

through user-defined functions (UDFs) in FLUENT, 

allows for the presence of large particles where particle 

collisions, rotation, adhesion, and other forces are 

accounted for using a hard sphere approach. Third, 

modifications to the volume of fluid (VOF) free surface 

model in FLUENT have been developed to simulate capillary-driven 

flow in an unsaturated porous region. 

The primary modeling goal was to study the spread 

of unbound liquid inside the voids formed by fibrous 

material, but the model also accounts for sorption (the 

physical bonding of moisture to the fiber), since the 

performance of many hygiene, food, and consumer 

products strongly depends on their sorption capacity. 

The flow of macroscopic particles 

in a hopper 

Technical advances at Fluent have progressed in two 

distinct directions. The traditional focus on more 

reliable and advanced physical models is being complemented 

by highly customized interfaces to fit the 

modeling needs of a specific application or equipment 

design. These advances, combined with the successful 

track record of CFD in many companies, have brought 

the full power of CFD to process engineers. 

The applicability of CFD to process industry applications 

is achieved through ongoing focused development 

and user-driven projects. For example, three customerdriven 

technology developments have been implemented 

recently that uniquely expand FLUENT’s capabilities. 

First, a population balance modeling effort was driven 

by a need to combine detailed fluid mechanics analysis 

with a better description of the size distribution of particles, 

bubbles, or droplets in a multiphase mixture. The 

In this special newsletter supplement, a diverse sampling 

of process industry applications is presented. 

Traditional applications such as mixing in stirred tanks 

are joined by mixing in microchannels. Reacting flow is 

covered in the context of thermal runaway (and a 

promising antidote), the cleanup of emissions, and 

the optimization of an ethylene cracking furnace. 

Multiphase flow examples include a fluidized bed and 

droplet breakup validations. The use of CFD for designing 

special purpose pumps is also reviewed. Finally, simulations 

of the extrusion of foam – a gas-impregnated 

polymer – are described. 

As these articles illustrate, the use of CFD is spreading 

to more and more applications within the process 

industries. With increases in computing capacity and 

the advances in technology currently taking place, this 

expansion can only continue in the years to come. 

Fluent News is published by 

10 Cavendish Court • Lebanon, NH 03766 USA 

603 643 2600 • www.fluent.com 

© 2006 Fluent Inc. All rights reserved. 

Editor: Liz Marshall 

Assistant Editor: Susan Wheeler 

Contributing Editors: Erik Ferguson and Keith Hanna 

Design: Lufkin Graphic Designs 

An iso-surface of liquid at two times, corresponding to a 10% saturation level 

predicted by FLUENT’s wicking model 

FLUENT, FiDAP, GAMBIT, POLYFLOW, G/Turbo, MixSim, 

FlowLab, Icepak, Airpak, and FloWizard are trademarks of 

Fluent Inc. Icepak and Airpak are joint developments of 

Fluent Inc. and ICEM-CFD Engineering. All other products 

or name brands are trademarks of their respective holders. 

S2 Fluent News · Spring 2006

MIXING 

The two-impeller system with conductivity probes 

Contours and vectors of velocity magnitude in a 

vertical plane mid-way between two baffles, 

computed by the sliding mesh model and the 

standard k-ε (top) and LES (bottom) turbulence 

models 

Tracing 

Homogenization 

By Michal Mo˘stĕk and Milan Jahoda, Department of Chemical Engineering, 

Prague Institute of Chemical Technology, Prague, Czech Republic 

THE BLENDING OF MISCIBLE LIQUIDS is a 

common mixing operation in the chemical, biochemical, 

food-processing, and other industries. 

Most frequently, it takes place in mechanically 

agitated tanks, where the miscible liquids are 

blended to a predetermined degree of homogeneity. 

One of the critical parameters of a mixer 

is the time of homogenization, or blending time. 

In practice, it is widespread to use empirical correlations 

derived from experiments to predict 

mixing times. However, these correlations have 

many limitations, mainly in the design of 

non-standard tank geometries and in cases with 

multiple impellers. 

Using CFD, the study of processes taking place in 

mixing tanks becomes easier and more economical 

than with the use of experiments. Thanks to 

the growing performance of today’s computers 

and with the help of other timesaving tools like 

the preprocessor MixSim, an elementary simulation 

of a mixing system can be performed within 

an hour. For more advanced simulations, the possibility 

of parallel computations and the availability 

of new and faster non-iterative solvers today 

enable a detailed study of mixing tanks to be performed 

in a range of days and weeks, where it 

would have taken months not long ago. 

In mixing tanks, where baffles are used to eliminate 

the central vortex and prevent air entrainment 

in the system, two methods of simulating 

the impeller rotation are most often used. The 

multiple reference frames (MRF) method is a fast 

steady-state method, which together with the 

k-ε family of turbulence models, provides timeaveraged 

information about processes taking 

place in the system. The transient sliding mesh 

(SM) method is much more accurate and suitable 

for the simulation of homogenization. For both 

methods, the tank is divided into a cylindrical 

part containing the impeller and a stationary part 

containing the baffles and tank walls. In the MRF 

method the impeller maintains a fixed position 

relative to the baffles, and the solution in the 

impeller region is solved in the rotating frame of 

the impeller. For the SM method, the grid for the 

impeller region rotates in time, so that transient 

effects, especially those applied to phenomena 

such as blending, can be captured. While 

the time-averaged k-ε models are often used in 

combination with the SM model, a transient 

turbulence model can also be used for the most 

comprehensive simulation of a transient process. 

When this level of detail is of interest, a transient 

turbulence model such as large eddy simulation 

(LES) can be used. To get the most out of this 

kind of modeling effort in terms of accuracy and 

precision, care must be taken when setting up 

the model, in terms of the size and density of the 

computational grid, the time step, and a suitable 

choice of sub-grid scale turbulence model. 

Two examples of liquid homogenization in a 

tank filled with water and equipped with either 

one Rushton turbine or two 6-bladed 45° 

pitched blade turbines mounted on a common 

shaft are presented below. For both cases, the 

tank diameter, T, is 0.29m and the shaft rotates 

at a constant speed of 300rpm. A second liquid 

(a saturated solution of NaCl) is introduced to 

the tank and the progress of blending is studied. 

Conductivity probes are used for experimental 

observations of tracer concentration, 

and CFD models are run for comparison. The 

measuring volume of the probe is approximately 

0.55cm 3 . For the one-impeller system, 

the probe is placed between the baffles, with 

an off-bottom clearance of T/4 and at a distance 

of T/20 from the tank wall. For the twoimpeller 

system, one probe is placed near the 

tank bottom, with the same off-bottom clearance 

and distance from the wall as in the previous 

case. A second probe is placed at a distance 

of T above the first probe. The tracer is 

injected just below the free liquid surface, at a 

horizontal distance of T/4 from the tank wall, 

opposite the probe. Output signals from the 


Fluent News · Spring 2006 

S3

MIXING 


normalized concentration 

normalized concentration 

2.0 

1.5 

1.0 

0.5 

0.0 

0.0 2 4 6 8 10 

time (s) 

2.0 

1.5 

1.0 

experiments 

mrf ske 

sm ske 

sm les 

0.5 

experiments 

mrf ske 

sm ske 

sm les 

0.0 

0.0 5 10 15 20 25 

time (s) 

The time traces of normalized concentration 

recorded (top) at one location in the one-impeller 

tank and (bottom) at two locations in the twoimpeller 

tank during the homogenization process, 

comparing three modeling approaches with five 

identical experimental runs; the sliding mesh with 

LES approach is the closest fit to the data 

probes are processed by a conductivity meter, 

digitized by an A/D converter and registered 

by a computer for further processing in the 

form of dimensionless concentration values. 

In the simulations, structured grids composed of 

non-uniformly distributed hexahedral cells were 

made in GAMBIT. The tank volume was decomposed 

into many smaller volumes in order to 

make a grid usable for structured meshing. In 

the case of the one-impeller system, the whole 

vessel was divided into 611,000 hexahedral 

cells (99 x 37 x 168 along the axial, radial 

and tangential coordinates), in the case of the 

two-impeller system, the mesh consisted of 

1,200,000 hexahedral cells (198 x 37 x 168 along 

the axial, radial and tangential coordinates). 

The first group of simulations of the flow field was 

performed using the MRF technique. By tracking 

the mixing of two fluids with equal properties, 

the solution of a single species equation could be 

used to follow the mixing progress. Converged 

solutions were obtained and then the flow field 

was “frozen” and only the unsteady solution of 

the passive scalar equation was performed. 

In the case of the SM and standard k-ε (SKE) 

turbulence model, the MRF solution was used as 

a starting point. The simulation was then 

switched to unsteady, with a time step set to 

0.001s and the non-iterative time advancement 

(NITA) option. When the periodic steady-state 

was reached (after 25s in the case of the 

one-impeller system and about 40s in the case of 

the two-impeller system), the homogenization 

simulation was initiated in the same way as 

described for the MRF simulation. The only difference 

was that the computations of tracer distribution 

in the vessels were solved together with 

the flow equations as the impeller mesh continued 

to rotate. 

In the case of the SM and LES approach, the MRF 

solution was again used as a starting point. The 

dynamic Smagorinsky-Lilly sub-grid scale model 

was chosen to capture the effects of turbulent 

eddies smaller than a cell size. Special attention 

was also given to the discretization schemes used. 

In particular, the bounded central differencing 

(BCD) scheme was used for discretization of the 

momentum and species transport equations, the 

body force weighted scheme was used for pressure 

discretization, and the fractional step method was 

chosen for pressure-velocity coupling. 

Comparison of the velocity fields calculated using 

the standard k-ε and LES models show the 

increased detail provided by the LES approach. 

For the two-impeller system, concentration maps 

at different times of the LES computation illustrate 

the complex dispersion of the tracer during 

the blending process. For both vessels, comparisons 

of the calculated and experimental concentration 

profiles indicate that the sliding mesh 

approach using LES provides the closest overall 

match to the probe readings throughout the 

blending process. 

Contours and vectors of velocity magnitude in a 

vertical plane mid-way between two baffles, 

computed by the sliding mesh model and standard 

k-ε (left) and LES (right) turbulence models 

1 sec 5 sec 10 sec 15 sec 

Contours of normalized tracer concentration in a vertical plane mid-way 

between two baffles after the tracer addition 


MIXING 



By Benoit Post, Fluent France, and Rajiv Lochan Rath, Fluent India 

THE MIXING OF SINGLE AND MULTIPHASE FLUIDS in stirred tank reactors 

is a common operation in many industries, such as chemicals, water treatment, 

pharmaceuticals, and petroleum. Understanding the fluid flow in these 

tanks is critical for equipment design, scale-up, process control, and economic 

factors. CFD is now being used routinely to provide this information, 

enabling engineers to select the best agitator design to obtain the desired 

process performance. Blending time evaluation is one of the key objectives of 

such CFD studies, and there are currently several numerical approaches that 

can be used for this purpose [1]. The objective of a project recently performed 

at Fluent France and Fluent India was to compare these different 

approaches in terms of quantitative results and CPU time. 


A cylindrical, flat bottom, stirred tank with 4 baffles and a non-standard, Rushtontype 

impeller with 4 radial blades was used for the simulations. Owing to the 

rotational symmetry of the geometry, a 90° sector of the tank was modeled. 

The different approaches studied made use of either the multiple reference 

frames (MRF) or sliding mesh (SM) model. For the MRF runs, a steady-state 

flow field was first computed and a transient calculation of a tracer species 

was performed using the “frozen” flow field. The first such blending calculation 

was done using the default settings, including the use of relative velocities. 

A second blending calculation was performed with absolute velocities. 

For this case, the paddles on the impeller were changed to interior zones, 

since this setting is more appropriate for walls moving normal to the fluid 

when absolute velocities are used. The walls exerting shear on the fluid (the 

impeller disk) were not changed. For the transient sliding mesh calculations, 

the flow field and tracking of the tracer species were computed simultaneously. 

Two solver options were used: the iterative time advancement (ITA) 

scheme and the non-iterative time advancement (NITA) scheme, which was 

introduced in FLUENT 6.2. The sliding mesh results are the most rigorous, so 

they served as a standard for comparison with the other methods. A correlation 

was also used to compare the results. 

Pathlines illustrate the two circulation loops 

in the vessel 

A steady state, MRF solution for the flow was first obtained. On this frozen 

flow field solution, the transient species calculation was performed after initializing 

a volume of tracer in the upper part of the vessel. The MRF flow field 

was also used as the starting point for the unsteady sliding mesh calculation. 

Before introducing the species, the sliding mesh calculation was done for a 

few cycles until periodic behavior was obtained. At this point, the species was 

introduced in the same location used for the MRF calculations, and the transient 

calculation of the flow field and species was performed. 

Results were analyzed globally in terms of the blending time, t 99 , or time 

required to achieve 99% uniformity in the tank. A published correlation for 

t 99 for a six-bladed Rushton turbine was used for comparison [2]. The blend 

time predicted by the Rushton correlation was found to be longer that that 

predicted by the CFD methods, a result that is due to the fact that the paddles 

on the impeller used have more surface area than a standard Rushton. 

In addition, the tracer species introduced from one location in the 90° model 

was equivalent to it being introduced from four locations in a 360° vessel. 

The tracer species initial locations at t=0 


S5

MIXING 


The tracer distribution after 1.5 (left) and 3.0 

(right) seconds for the MRF simulation 

mass fraction 

mass fraction 

6E-03 

5E-03 

4E-03 

3E-03 

2E-03 

1E-03 

SM + ITA 

SM + NITA 

MRF – ABS 

MRF – REL 

0E-03 

0 2 4 6 8 10 12 14 

time (s) 

Mass fraction of the tracer species computed by the 

SM and MRF methods, recorded at points near the 

top of the vessel (top) and near the impeller 

(bottom) 

6E-03 

5E-03 

4E-03 

3E-03 

2E-03 

SM + ITA 

SM + NITA 

MRF – ABS 

MRF – REL 

The correlation assumes that the species is introduced from one location, usually 

at the surface of the liquid. Comparison of the CFD methods revealed that the MRF 

model predicted longer mixing times than the sliding mesh model. The two 

MRF approaches predicted similar blend times, as did the two SM approaches. 

Correlation MRF with MRF with Sliding Sliding 

for 6-bladed relative absolute Mesh Mesh 

Rushton velocities velocities with ITA with NITA 

turbine (default) scheme scheme 

Blending time(s): t 99 19.0 (+/- 6) 15.8 (+/- 4) 15.3 (+/- 4) 14.8 (+/- 3) 14.9 (+/- 3) 

The various approaches were also compared by monitoring the CPU time required 

to reach t 99 . It was found that the sliding mesh model using the new NITA scheme 

takes about 4 times less CPU time than the sliding mesh model using the ITA 

scheme. The steady-state MRF solutions are much quicker, requiring about 2.5 

times less CPU time than the sliding mesh model with the NITA scheme. 

The results were also analyzed locally by monitoring the mass fraction of the tracer 

species at several sensor locations. Two locations in particular are of interest: near 

the tip of the blade and near the top of the tank. Near the impeller, the 

sliding mesh case picked up the blade passing frequency, and in addition, a low 

frequency oscillation of greater amplitude that was also observed for the MRF 

approaches. This low frequency oscillation was also picked up at the top of the 

tank by the sliding mesh and MRF approaches, although the blade passing 

frequency was not (for the former). The low frequency oscillation is indicative of a 

macroscopic exchange of the tracer material between the upper and lower regions 

of the vessel. This type of result is not uncommon when a radial impeller is used 

and distinct circulation patterns develop above and below the impeller. 

Overall, the MRF model is an economic approach for delivering sufficient blending 

time information. If, however, an accurate representation of the transient dispersion 

of a tracer is required – for reacting flows, for example – the sliding mesh approach 

is necessary, and the NITA scheme in FLUENT now makes sliding mesh calculations 

much more affordable. 

References: 

1 Marshall, E.M. and Bakker, A.: Computational Fluid Mixing. In: The Handbook of Industrial 

Mixing, Ch. 5. Paul, E., Atiemo-Obeng, V. and Kresta, S., Editors, John Wiley, 2004. 

2 Fasano, J.B., Bakker, A. and Penney, W.R.: Advanced Impeller Geometry Boosts Liquid 

Agitation. Chemical Engineering, August 1994. 

1E-03 

0E-03 

0 2 4 6 8 10 12 14 

time (s) 

Iso-surfaces of tracer mass 

fraction show the polarized 

species distribution in the 

vessel, even after the liquids 

are well mixed 


MICROREACTORS 

Liquid Mixing 


By M. Hoffmann, M. Schlueter, N. Raebiger; University of Bremen, 

Institute of Environmental Process Engineering (IUV), Bremen, Germany 

MICROREACTORS ARE BASIC components 

of microfluidic systems for chemical and biochemical 

applications and are an important area 

of research in fields such as analytical chemistry, 

chemical engineering, and life science. They are 

well suited for the controlled mixing of reactants 

and show great potential for optimizing conventional 

processes. The large area-to-volume ratio 

of microreactors allows for a higher yield and 

selectivity than conventionally designed processes. 

To take advantage of the full potential of this 

ambitious technology, a fundamental understanding 

of the transport processes on the relevant 

time and length scales is necessary. One 

approach is through the application of CFD. An 

example that is currently undergoing study using 

numerical simulation is that of a T-shaped 

micromixer with a rectangular cross section. 

High resolution CFD simulations have been 

performed using FLUENT 6.1 at the Institute of 

Chemical Engineering at the University of 

Paderborn, Germany [1]. The computational 

domain is meshed by a block structured grid with 

about 970,000 cubic cells. A more refined computation 

is also being performed on a grid of 

about 9 million cells. For the entrance region in 

the mixing channel, inside the mixing zone, the 

grids have a spatial resolution of 2.5 and 1µm 

respectively. The velocity field is given by the stationary 

solution of the Navier-Stokes equations 

using a velocity profile of fully developed, rectangular 

cross section duct flow at both inlets. At the 

outlet, the pressure is set to a reference value. All 

channels are closed at the top and a no-slip 

boundary condition is applied to all walls. The 

properties of water at 20°C are used and a tracer 

is added at one of the inlets. The species transport 

model is used to track the motion of the 

tracer through the micromixer. 

In addition to using a commercial CFD program 

for numerical flow visualization, microscale fluid 

flow visualization is an important tool for acquiring 

localized flow information within these 

devices. By means of two non-invasive measurement 

techniques, micro-laser induced fluorescence 

(micro-LIF) and micro-particle image 

velocimetry (micro-PIV), the concentration and 

velocity fields have been measured. 

The tracer distribution at different cross sections 

enables a quantitative analysis of the mixer to be 

performed. To construct the tracer distribution, a 

confocal laser scanning microscope (CLSM) is 

used. The CLSM can produce image slices with a 

minimum thickness of 2 microns. By combining a 

series of these slices, a three-dimensional rendering 

of the mixing quality in the mixing channel 

is possible. The mixing quality can then be quantified 

using Danckwerts’ intensity of segregation 

[1]. Other observations made in the CFD analysis 

and experimental tests include a secondary flow 

in the form of a vortex pair in the entrance 

region, which results from instabilities caused by 

centrifugal forces. 

The results are fundamental for improving the 

design rules for static micromixers and have 

enabled a validation of the CFD results. 

Acknowledgements: 

The authors gratefully acknowledge the CFD work 

done by our collaborators, Dieter Bothe, Center of 

Computational Engineering Science, RWTH Aachen, 

Germany and Hans-Joachim Warnecke, Institute of 

Chemical Engineering, University of Paderborn, 

Germany. 

References: 

1 Bothe, D., Stemich, C. and Warnecke, H.-J.: Fluid 

Mixing in a T-shaped Micro-mixer. Chem. Eng. Sci. 

61, 2950-2958, 2006. 

Visit us at 

ACHEMA 2006: 

Hall 1.2, Stand E9-F10 

Tracer profile in the y-z plane (top) on a crosssectional 

slice through the mixing channel 

(bottom) at x=100 µm computed using CFD; the 

Reynolds number for the mixing channel is 186 

(corresponding to an inlet velocity of 1.4 m/s for 

each inlet) 

Courtesy of the Institute of Chemical Engineering, University of Paderborn 

Velocity field along a mixing channel with a width 

of 400 µm and height of 200 µm using micro-PIV; 

the measurement plane (x-y plane) is at z=143 µm, 

and flow is from left to right 

Concentration field in the mixing channel of a 

T-shaped micromixer with a width of 400 µm and 

height of 200 µm, constructed using image slices 

from a confocal laser scanning microscope and 

micro-LIF in the y-z plane, with an x range of 270 

to 782 µm; the Reynolds number of mixing 

channel is 207 and the flow is from back to front 



S7



Preventing 

Runaway 

Reaction 

Accidents 

By D. Dakshinamoorthy and J.F. Louvar, Chemical Engineering Department, 

Wayne State University, Detroit, Michigan, USA 

RUNAWAY REACTIONS ARE AN ONGOING PROBLEM in the chemical 

industry, where they account for 26% of major accidents. Runaway 

reactions generate a sudden excess amount of heat, which can lead to 

an explosion. They can be stopped in two ways: by the addition of cold 

diluents and by the addition of an inhibitor, a chemical that acts to suppress 

the runaway reaction. The technology that involves the use of 

inhibitors is called shortstopping. Power failures are one of the main 

reasons for runaways, and after a power failure, the process of adding 

an inhibiting agent and mixing it with the reactor contents becomes a 

major problem in the shortstopping process. Jets or impellers, driven by 

a small generator, can be used for mixing under such circumstances. 

From a design standpoint, jet mixing is one of the simplest methods to 

achieve mixing. In jet mixing, a part of the liquid in the tank is drawn 

out through a pump and returned to the tank as a high-velocity jet 

through a nozzle, resulting in fluid mixing. In a recent project [1], CFD 

was used to compare the efficiency of jet mixers with impeller stirred 

vessels in shortstopping runaway reactions. On the basis of equal power 

consumption, this comparative study showed that jet mixers were ineffective 

when used for shortstopping, unless certain factors could be 

optimized. Due to the hazardous nature of runaway reactions, these 

factors cannot be determined with lab scale or pilot plant scale experiments, 

but CFD can be used to carry out virtual experiments instead. 

Using FLUENT, mixing with a jet mixer was first investigated for different 

nozzle diameters and angles of injection. To account for the external 

circulating pump, user-defined functions (UDFs) were used to 

ensure that the mass fractions of all species entering the vessel through 

the inlet were equal to those leaving through the outlet at each 

timestep. Before simulating the reacting flow of the shortstopping 

process, the flow model alone was validated. Since overall mixing controls 

the process, the predicted mixing times were compared with the 

available experimental correlations [2, 3] and found to be in excellent 

agreement. 

Solution domain and pathlines for the jet 

mixer studied 

The converged flow results of the best jet configuration were then used 

for subsequent simulations of the runaway and inhibition reactions. 

Laminar volumetric reactions were modeled using UDFs. Guibert et al. [4] 

documented the kinetics of the considered runaway reaction. Before analyzing 

a specific runaway scenario, reactor conditions were varied and 

runaway behavior was studied. From all of the cases considered, the one 

with the fastest temperature increase was selected for further study, since 

it would be the most difficult to shortstop. For this scenario, as soon 

as the reactor temperature reached 450K, the inhibitor was added and 

the species transport equations for the inhibition reaction were solved 

simultaneously with the runaway reaction. 

The results were used to identify the major and minor factors that contribute 

to effective shortstopping when using a jet mixer. These factors 

include the location for adding the inhibitor; the amount of the 

inhibitor; the rate of the inhibition reaction; the power input; the use of 

a cold diluent; and the use of multiple nozzles. The temperature distribution 

in the reactor after the shortstopping process and the decrease 

in the average reactor temperature were used to assess the importance 

of each factor. 

S8 Fluent News · Spring 2006 

Grid near the inlet 

For example, the temperature distribution on the mid-plane and an 

iso-surface of temperature equal to 500K (temperatures over 500K were 

considered hazardous) for two power levels was used to illustrate the 

importance of power input. The hotspots, or high temperature regions 

over 500K, decrease in size as the power is increased, which indicates 

improved shortstopping. In addition to the power input, the decrease in


mixing time (sec) 

10 3 

10 2 

10 1 Jet Reynolds number 

10 2 10 3 

10 4 

experiment 

FLUENT 

Comparison of the predicted mixing time and experimental data for a 

range of jet Reynolds numbers [2] 

the final reactor temperature for the other factors was computed as well. When 

compared, the major factors that were found to contribute most to effective 

shortstopping were the use of cold diluents and the use of multiple nozzles. 

The factors found to be of less importance were the rate of the inhibition reaction, 

the location of the inhibitor injection, and the amount of inhibitor. The 

study clearly demonstrated the value of using CFD simulations in situations that 

are experimentally prohibitive. 

Kinetic Details of 

Runaway and 

Inhibition 

Runaway Reaction 

Guibert et al. [4] studied the kinetics of the propylene oxide polymerization 

reaction. The rate of the reaction is defined as a function of temperature, 

propylene oxide concentration, and catalyst concentration. 

Monomer polymerizes to polymer in the presence of a basic catalyst. The 

kinetic expressions for the monomer concentration and temperature are 

as follows: 

1. 

2. 


Acknowledgements: 

The authors acknowledge NSF for their support of this particular project, and Dr. Vivek. 

V. Ranade and his student Dr. Avinash. R. Khopkar of IFMG, National Chemical 

Laboratories, Pune, India, for their technical contributions and inspiration. 

References: 

1 Dakshinamoorthy, D., Khopkar, A.R., Louvar, J.F. and Ranade. V.V.: CFD Simulations 

of Shortstopping Runaway Reactions in Vessels Agitated with Impellers and Jets. 

Accepted in the Journal of Loss Prevention in the Process Industries, 2005. 

2 Lane, A.G.C. and Rice, P.: An Investigation of Liquid Jet Mixing Employing an 

Inclined Side Entry Jet. Transactions of Institute of Chemical Engineers, 60, 171-176, 

1982a. 

3 Lane, A.G.C. and Rice, P.: Comparative Assessment of the Performance of Three 

Designs for Liquid Jet Mixing. Industrial & Engineering Chemistry Process Design 

and Development, 21, 650-653, 1982b. 

4 Guibert, M.R., Plank, A.C. and Gerhard, R.E.: Kinetics of the Propylene Oxide – 

Oxypropylated Glycerol Reaction. Industrial & Engineering Chemistry Process 

Design and Development, 10 (4), 497-500, 1971. 

Inhibition Reaction 

The added inhibitor neutralizes the basic catalyst. The kinetic expression 

for the inhibition reaction is as follows: 

3. 

Kinetic Data for 

Inhibition and 

Runaway Reactions 

Activation Energy 

Heat of Reaction 

Pre-Exponential Factor 

Specific Heat Capacity 

Pre-Exponential Factor 

Gas Constant 

E = 6.96E+08 J/kgmol 

H = -1.63E+06 J/kg(monomer) 

k o = 9.5E+11 kg(total)/(kg(cat)-hr) 

c p = 2930 J/kg(total)-K 

k 1 = 9.5E+11 kg(total)/(kg(inh)-hr) 

R = 8314.34 J/kgmol-K 

< 450K > 550K 

Contours of temperature on the mid-plane and an iso-surface of tempetature at 

500k illustrate the temperature distribution after shortstopping with less (left) 

and more (right) power input 


S9

EMISSIONS 


Scrubbers for Flue Gas Cleanup 

By Christoph Hochenauer, Austrian Energy & Environment AG, Raaba/Graz, Austria, 

Martin Demuth, Institute for Process Technology & Industrial Environmental Protection, 

University of Leoben, Austria, and Wolfgang Timm, Fluent Germany 

The mesh structure 

WITH TODAY’S INCREASINGLY RESTRICTIVE environmental 

regulations, there is an ongoing need for state-of-theart 

modeling of pollutant formation and removal in industrial 

processing units. A particularly important issue is the sulphur 

dioxide (SO 2 ) content in the flue gases of power plants. A 

typical approach for removing this hazardous component is 

through absorption by limestone slurry droplets in so-called 

flue gas scrubbers. Scrubbers consist of large towers in which 

up to several thousands of nozzles provide a homogenous 

distribution of droplets within the gas flow. This leads to 

the highest possible absorption rate for a given slurry mass 

flow. To optimize the nozzle configurations for scrubbers, 

CFD simulation is one of the best tools to use. 

Mesh in the spray bank zone 

The absorption of SO 2 is a complex process with several 

mechanisms to be considered. The continuous phase (gas) and 

discrete phase (droplets) have to be calculated as well as the 

interaction between them. The momentum and temperature 

of the droplets affect the gas flow and vice versa. 

Additionally, mass transfer between the droplet and gas phases 

has to be taken into account. Depending on the difference 

in the partial pressure of water vapor in the two phases, there 

might be evaporation from the droplets or condensation onto 

the droplet surfaces. Rapid changes in temperature cause both 

evaporation and condensation to have a significant influence 

on the SO 2 absorption rate, which itself is governed by a complex 

ion chemistry mechanism within the droplets. The heat 

and mass transfer rates are dependent on the droplet surface 

area, which changes during evaporation or condensation, or 

whenever there is splashing at the walls. To account for the 

latter, an appropriate wall interaction model also has to be part 

of the CFD simulation. With FLUENT’s user-defined function 

(UDF) capability, a set of models addressing these issues has 

been implemented, and a test case with typical scrubber 

conditions has been created to illustrate the capabilities. 

A cylindrical scrubber with two sets of eight radial spray bars 

was considered. The grid was built using the polyhedral mesh 


EMISSIONS 

capability of FLUENT 6.3. The original mesh consisted of a total of 1.25 million 

cells, which were reduced to 250,000 cells after the tetrahedral elements 

were converted to polyhedra. Using sizing functions, a sufficient grid resolution 

in the near wall regions of the spray bank was ensured. A flue gas mass 

flow rate of 65kg/s was set at an inlet at the bottom of the unit with SO 2 and 

water vapor mass fractions of 0.0024 and 0.12 respectively, and the remainder 

consisting of air. Sixty-six full cone nozzles were distributed in a circular 

pattern on the sixteen radial pipes for a total slurry mass flow rate of 330kg/s. 

Both the gas flow and the droplets started with an initial temperature of 

316K. For the droplets, the dispersed phase model (DPM) was used with a 

large number of trajectories, whose interaction with the gas phase was evaluated 

separately. The RNG k-ε model and the stochastic tracking method 

were used to incorporate the effects of turbulent fluctuations on both 

phases in the simulation. 

In a surrounding air flow at ambient temperature, a water droplet is subject 

to a coupled heat and mass transfer process. With increasing velocity 

difference between the two phases the transfer is enhanced and can be 

calculated with the Ranz-Marshall law, which can be used for SO 2 absorption 

as well as vaporization and condensation [1]. Since the latent heat of water 

Contours of water vapor content (left), SO 2 mass fraction (middle), and vertical 

velocity (right) on four slices within the scrubber; the flow in the unit is from 

bottom to top 

is quite high, the droplet temperature change along its trajectory cannot be 

neglected, since a temperature change of 10K can significantly affect the SO 2 

partial pressure at the droplet surface. 

When droplets hit the wall there are several possibilities of what can occur. 

Under certain conditions a droplet can become part of the liquid film on the 

wall and in other instances there may be splashing and breakup of the 

droplets. The latter results in an increased number of droplets with a larger 

total surface area, a change that again significantly affects the momentum, 

heat and mass transfer rates. For this application, it was assumed that the wall 

interaction behavior was governed by dimensionless Weber and Reynolds 

numbers [2] and a model was developed accordingly. 


The removal of SO 2 in the droplets is governed by a reaction with limestone, 

which can be described by the net reaction: 

Droplet temperature evolution along some of the droplet trajectories 

Additionally there are several dissolution reactions, since the formation of sulphurous 

and carbonic acid results in an ion chemistry mechanism that 

depends on the pH-value and the limestone and SO 2 concentrations of the 

droplets [3]. This was implemented by means of an externally evaluated 

lookup table that was coupled with the discrete phase using FLUENT’s particle 

scalar UDF, which allows additional droplet properties to be defined. 

Once the total sulphur content in a droplet has been evaluated, the new concentration 

of each ion is obtained from the table and stored in the respective 

DPM scalar. With this information the calculation of the SO 2 partial pressure 

at the droplet-gas interface and the respective sink terms is possible to ensure 

SO 2 mass conservation in both phases of the domain. 

Droplet splashing and breakup at the walls; the droplet trajectories are colored by 

particle diameter, so the red trajectories occur before splashing and breakup occur 

The results obtained from the simulation are in very good agreement with 

real-life scrubbers. Austrian Energy & Environment AG now includes FLUENT 

with these special models in their scrubber design workflow. The polyhedral 

cell technology has proved to be a significant improvement in terms of meshing 

flexibility and simulation time, enabling engineers to perform a complete 

scrubber simulation within a few days. The reliability of the results has meant 

that they need to rely less on field tests as well. 

References: 

1 Aguayo, P. and Weiss, C.: Enthalpy Two Way Coupling for Near Vapor Saturated 

Polydispersed Spray Flows. 5th International Conference on Multiphase Flow, 

Yokohama, May 30 - June 4, 2004. 

2 Mundo, C. and Tropea, C.: Numerical and Experimental Investigation of Spray 

Characteristics in the Vicinity of a Rigid Wall. Experimental Thermal and Fluid 

Science, 15:228-237, 1997. 

3 Weiss, C., Maier, H. and Bärnthaler, K.: Modelling of the Mass Transfer Processes in 

the Chemisorption of Flue-Gas Components by Sprays. Proceedings of the ILASS- 

Europe 2001; Zürich, 2-6 September 2001. 


S11

FURNACES 


Ultra-low NO x Burners 

By Robert J. Gartside and Peter R. Ponzi, ABB Lummus Global, Bloomfield, New Jersey, USA, and David G. Schowalter, Fluent Inc. 

wall burners 

hearth burners 

convection 

section 

The geometry of the ethylene cracking furnace 

cracking coils 

Get 

ENGINEERS AND MANAGERS involved in chemical and 

hydrocarbon processing are keenly aware of equipment efficiency 

and plant productivity, and the ongoing need for improvement 

in these areas. In ethylene production furnaces, light hydrocarbons 

are cracked in tubes that are suspended in a combustion 

chamber. The tubes have very short residence times. The critical 

parameters in the operation of this furnace are the transfer of the 

heat of cracking to the tube side hydrocarbons, control of the 

tube metal temperatures for prolonged run length, and the 

reduction of pollutants such as NO x to meet regulatory statutes. 

In the radiant section of a typical furnace, combustion heat is 

provided by burning fuel in hearth burners (located on the 

floor of the furnace and firing vertically) and in wall burners 

(positioned along the wall and firing radially along the wall). 

Temperature contours in the original burner 

configuration (left) and the first ultra-low NO x 

design (right) 

The John Zink Company has developed an ultra-low NO x burner 

(20 to 30 vppm) in which a portion of the fuel is premixed 

with all of the air in both the hearth and wall burners. The 

remaining fuel needed to achieve the firing rate is introduced in 

a staged manner to control the temperature of the combustion 

gases and thereby minimize the production of NO x . This is done 

through staged ports located in front of the hearth tile at the 

furnace floor. When the introduction of this burner technology 

was considered for a client’s ethylene production furnace, it was 

suspected that, when compared to the original conventional 

burners that generally produce straight vertical flames attached 

to the furnace wall, these lean premixed burners might produce 

shorter flames that could “roll over” at the bottom of the firebox 

and impinge on the tubes. The negative impact of rollover would 

be the creation of hot spots on the process tubes and subsequent 

increase in coking within the tubes and shortening of tube life. 

To investigate this possibility, a realistic CFD study was conducted 

to compare the performance of the low NO x and original 

burners. The detailed CFD models – one for each type of burner 

– included locally refined meshes around the burner inlet ports 

and process tubes. Combustion was simulated on the firebox 

side to provide the heat generation. A reacting model for the 

hydrocarbon cracking that occurs inside the tubes was also 

included, as were the effects of turbulence and radiation. Overall, 

there was sufficient detail to accurately reflect the heat absorption 

and thus provide realistic tube heat fluxes and coil metal 

temperatures. The models contained between two and five 

Temperatures on three cross-sectional planes in the 

original burner configuration (left) and the improved 

ultra-low NO x design (right) 


MULTIPHASE 

Cracking 

million computational cells depending on the 

type of burner modeled, and they required up 

to a week’s worth of CPU time on a high speed 

computer cluster. 

The initial simulations compared the heater with 

the original burners to the heater with the new 

burners where the new burners were simply 

located at the same position as the original burners. 

The results of this study indicated that, while 

the original burner design provided straight 

flames, the new low NO x configuration resulted 

in flames that impinged on the tube surfaces. 

Based on these results, an optimization study 

using CFD was done to develop design modifications 

that would allow the ultra-low NO x burner 

to perform acceptably in the heater. These modifications 

included redirecting the angle of the 

fuel that was being injected through the ports in 

front of the new burners and relocating the burners 

on the furnace floor to provide lateral spacing 

into which the combusting gas could expand, 

thereby reducing the tendency for the flames to 

roll into the tubes. The modifications served to 

straighten the flames, making them more in line 

with those of the original burners. While the new 

design produces more diffuse flames than the 

original burner design, it is much improved from 

the first ultra-low NO x design. 

The new burner configuration, optimized by 

CFD, has been installed in the client’s furnace. 

Flame quality and run length have been excellent 

and low NO x operation has been experienced. 

The start-up was smooth and downtime was 

minimal. 

Acknowledgement: 

The authors wish to acknowledge the cooperation 

of the John Zink Company in the low NO x burner 

modification CFD studies. 

Controlling Droplet 

Size Distribution in 

Emulsions 

By L. Srinivasa Mohan, Fluent India, and Ahmad Haidari and Aniruddha Mukopadhyay, Fluent Inc. 

Emulsions are an important class of materials produced 

and handled by the chemical, food, pharmaceutical, 

and cosmetic industries. They consist 

of two immiscible liquids, one dispersed in the 

other. The properties of an emulsion are based 

on the droplet size distribution (DSD) of the 

dispersed phase. Because they are often thermodynamically 

metastable, there is a persistent 

threat that the texture of the emulsion will be 

altered during the course of preparation or packaging, 

or during the subsequent shelf life. Many 

processes over widely varying length scales could 

cause the DSD to change, and it is important that 

they be well understood so that the emulsion 

quality can be maintained. 

For an ongoing project at Fluent, several aspects 

of droplet behavior have been studied using the 

volume of fluid (VOF) model. Some of the results 

have been compared to experiments that were 

carried out on microfluidic devices, where a precise 

droplet size distribution could be generated. 

For example, the generation of droplets at a 

T-junction using two streams of immiscible liquids 

has been simulated. Droplets of uniform size 

were rapidly and reproducibly produced at the 

junction as a result of the surface tension and the 

shearing motion of the fluid in the main channel, 

in agreement with measurements. 

In another study, the geometrically mediated 

breakup of droplets in a microfluidic device [2] 

was simulated. By changing the length of the 

arms of the T-junctions, the droplet can be split 

into daughter droplets of unequal size. By using a 

network of asymmetric T-junctions, emulsions of 

a given DSD can be produced. Both 2D and 3D 

simulations matched the qualitative and quantitative 

aspects of the experiments, such as the size 

of the daughter droplets for a given T-junction 

and the critical parameters required for droplet 

breakup as a function of capillary number. 

References 

1 Nisisako, T., Torri, T. and Higuchi, T.: Lab Chip. Vol 

2, no 1. pp 24-26, 2002. 

2 Link, D.R., Anna, S.L., Weitz, D.A. and Stone, H.A.: 

Phy. Rev. Lett. Vol. 92, pp 1178-1180, 2004. 

1E-07 

8E-08 

6E-08 

4E-08 

2E-08 

volume of droplet (m 3 )1.2E-7 

0 

0.0 

continuous phase flow 

dispersed phase flow 

Schematic of the experimental setup [1] 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

time (s) 

Volume of droplets produced at the T as a 

function of time 

Droplet breakup at a symmetric (left) and 

an asymmetric (right) T-junction 



S13

MULTIPHASE 


Understanding 

Fluid-Bed 

Coating 

Rod Ray, Lisa Graham, Rick Falk, Josh Shockey, and Leah Appel, 

Bend Research Inc., Bend, Oregon, USA, and Kumar Dhanasekharan, 

Fluent Inc. and L. Srinivasa Mohan, Fluent India 

DURING THE PAST DECADE, the use of fluidized bed systems for 

coating has developed to a point where this equipment is now used 

for applications in industries ranging from chemical to pharmaceutical to 

agriculture. The appeal of fluidized beds for coating is due to the high energy 

and mass transfer rates that are available in such a system. There are 

three types of fluidized beds that are typically used for this purpose: 

top-spray, tangential-spray, and bottom-spray. In a recent joint project 

involving engineers at Bend Research and Fluent, a fluidized bed with a 

bottom-spray of the Wurster type was studied. 

Fluid bed coaters are used to obtain controlled release or delayed release 

particles in pharmaceutical and agricultural applications. For these types of 

coatings, it is important to have control of both coating quality and coating 

uniformity. Typically, this process is run in batch mode with particles 

circulating through the coating zone multiple times in order to achieve the 

desired coating level. In this process, the particles are entrained in a high 

velocity gas, they pass through the Wurster column where coating is applied 

via a two-fluid atomizer, they then dry in the expansion chamber and 

fall back down to the bed to repeat the process. FLUENT has been used to 

help understand the process, design process conditions that yield products 

within an acceptable variation, and understand the effect of various 

parameters on product variability and quality. 

coating solution 

atomization air 

fluidization air 

Wurster gap 

Schematic illustration of a process stream in a 

bottom-spray Wurster fluidized bed coater 

Accounting for the presence of a secondary phase within CFD software is 

well established. Some of the more recent advances enable simulations of 

flows with very high solid loading, such as hoppers and chutes; flows with 

large particles, such as particle milling machines; gas-solid flows with particle 

size variation, such as polymerization reactors; and flows where the effects 

of particle shapes and particle mechanics are important. For the Wurster bed 

simulation, the Eulerian granular model in FLUENT was used. The results 

have led to a better understanding of the uniformity of coating, spray 

patterns, and fluidization behavior. Parameters that control the process 

efficiency and coating uniformity were studied. These included the 

equipment geometry, air flow rate, and other spray-related characteristics. 

Of particular importance for the equipment geometry is the Wurster gap, a 

small passage at the base of the unit between the inner and outer (annular) 

columns. During operation, the particles in the bed are fluidized and 

coated primarily in the inner column, where the fluidization velocity is 

highest. As a result, a steady fountain of solid particles is produced, and after 

reaching the upper region of the device, these particles stream down the 

walls of the outer section prior to being entrained through the gap and 

re-entering the primary fluidization region. 

The results indicated that there is a steady solids flux through the larger gap, 

but in the case of the smaller gap, the flow of solids through the gap is 

choked periodically and the bed does not maintain a steady fountain. This 

creates a non-uniform solids flux in the spray region, which could lead to 

problems with coating uniformity. 

Contours of particle volume fraction for two different Wurster gap designs: 

wide (left) and narrow (right), for a similar air flow rate 


PUMPS 

Pathlines through one of 

Tuchenhagen’s pump designs 

Pumping out 

New Designs 

More Quickly 

By Matthias Südel, Tuchenhagen GmbH, Büchen, Germany 

TUCHENHAGEN has been producing process components 

for the brewery, beverage, dairy and food industries for more 

than seventy years. In addition, the valves, pumps, and cleaning 

devices they produce are also used in the cosmetics, 

healthcare, pharmacy, and fine chemicals segments, where 

hygiene and sterility play a major role. The company, which 

has been a member of the GEA group since 1999, introduced 

the first twin seat valve with a mixproof technology in 1967. 

Customers’ expectations about the quality of process technology 

used have been increasing ever since. Many products 

must meet the stringent requirements outlined in the 

European Hygienic Equipment Design Group (EHEDG) and 3A 

sanitary standards. All devices must be capable of cleaning 

and flushing and must be free from dead space (clearance 

volume). 

In the past, designers made predictions about the cleaning, 

purification, and purging capabilities of the new components 

only after expensive technological trials. Today, CFD is capable 

of mapping these prototypes, allowing designers to examine 

every corner of the valve, pump, or cleaning device. 

FLUENT is now used to accelerate process safety and the 

durability of Tuchenhagen’s components. The software makes 

flow patterns visible and tracks even the smallest of problem 

areas in the course of computations. As a result, the development 

costs associated with the recent generation of pumps 

have decreased by seven percent compared to the conventional 

development process. The time required to reach the 

mass production phase has been reduced by six to eight 

weeks for each new design. 

For many years, engineers at Tuchenhagen could design and 

build a new pump based solely on their years of experience. 

With CFD, the company now has at its disposal a fast and costeffective 

method to support this experience with numerical 

data. Using this principle, Tuchenhagen developed three new 

pumps last year. Their approach is to carry out a computation, 

then built the prototype and make measurements. 

To improve upon this process, they have defined special 

configuration attributes in the CFD software. In two cases, the 

pumps were modeled using CFD, then tested at the operating 

point, and quickly put into mass production. A typical pump 

simulation makes use of a hybrid mesh of about 1 million cells. 

The steady-state MRF model is used to simulate the rotation of 

the moving parts. The standard k-ε model with enhanced wall 

treatment is used for turbulence. 


A VARIVENT ® mixproof 

valve Type R 

Velocity magnitude on different 

planes of a VARIVENT ® valve 

Type R 

The numerical simulations have reduced the time required 

to go from the first prototype to the actual mass-produced 

product. Additionally, material costs for high-grade steel 

during the prototyping phase do not need to be considered. 

Typically, making the first sample takes up to two weeks, and 

measurements take up another two to four weeks depending 

on the components. This is partly due to the fact that the 

cleanability of the products, which is so important to 

Tuchenhagen, must be repeatedly demonstrated in product 

trials. While it is not possible to entirely eliminate test runs, the 

company can now examine predictions of the wall shear 

stress, which is the deciding quantity for cleanability, since it 

is related to the ability of the fluid to dislodge dirt particles. 


S15

EXTRUSION 



Simulation for Foam Extrusion 

By Hirohisa Shiode and Takeharu Isaki, Mitsui Chemicals, Inc., Material Science Laboratory, Chiba, Japan 

MITSUI CHEMICALS and Mitsui Chemicals 

Group produce a wide variety of chemical products 

from catalysts to polymers and fabricated 

products like films and fibers. The Computational 

Science Department at the Material Science 

Laboratory investigates simulation technologies 

and applications to research and development in 

the areas of chemistry, engineering, polymeric 

material design and polymer processing. 

Since 1993, POLYFLOW has been used for polymer 

processing applications involving various 

rheology models, boundary conditions, and free 

surface flows, such as extrusions through 

complex and multi-layered dies and profile 

extrusions. One area of interest is foam extrusion, 

which includes gas dissolution into the polymer 

melt, viscosity reduction, nucleation with pressure 

drop, and bubble growth. The foam properties 

are closely related to cell structures, which are 

determined by the process conditions. A simulation 

method for gas-dissolved polymer flow has 

been developed for predicting these cell 

structures and the optimal die design. It makes 

use of a viscosity model, which is based on free 

volume theory [1,2] and which takes into 

account the temperature, pressure, and gas 

concentration in the melt. The model is 

implemented in POLYFLOW by means of userdefined 

functions (UDFs), and is used for 3D 

simulations of the flow through the die. To 

account for the shear-thinning behavior of the 

material, the Cross-Carreau model is adopted. 

The pressure distribution in a coat hanger die with 

a gas concentration of 1% 

pressure drop (MPa) 

35 

30 

25 

20 

15 

10 

5 

0 

0.0 

The geometry of a coat hanger die 

0% gas 

1% gas 

2% gas 

3% gas 

0.2 0.4 0.6 0.8 1.0 

dimensionsless length 

The pressure drop 

through a coat 

hanger die along the 

centerline for gas 

concentrations 

ranging from 0 to 3% 

As an example, the customized model has been 

applied to the flow through a coat hanger style 

die. The results indicate that the pressure drop 

through the die decreases with increasing gas 

concentration as a result of the associated drop in 

viscosity. This result is consistent with experimental 

observations. Further work is now being conducted 

to couple the flow simulations with nucleation 

and cell growth. 

References 

1 Shiode, H. and Isaki, T.: Polymer-Supercritical Fluid 

Systems and Foams. Dec., Tokyo P-22 188, 2003. 

2 Willams, M.L., Landel, R.F. and Ferry, J.D.: J. Am. 

Chem. Soc., 77, 3701, 1955. 


AEROSPACE 

The impact of a rear-mounted nacelle on the top surface 

of a wing is illustrated by static pressure contours 

Nacelle Impact 

on Aircraft Wing & Fuselage 

By Zhu Jie, AVIC 1 (Aviation Industries of China) Commercial Aircraft Company, Shanghai, China 

THE GENERAL CONFIGURATION and aerodynamic 

design integration of the nacelle on a 

commercial aircraft have an important influence on 

economy, safety, comfort, and performance. It is a 

complex yet important task to study the aerodynamic 

interference between the wing/body and 

engine nacelle and develop techniques for integrating 

these components during the design phase. 

These considerations have played a role in the 

development of the advanced regional jet ARJ21 

from China’s AVIC 1 Commercial Aircraft Company 

(ACAC). At ACAC, FLUENT was introduced during 

the pre-development phase, and it continues to 

play a major role in design, engine selection, and to 

save time spent on wind tunnel tests. 

Before applying the turbulent Navier-Stokes 

equations to the complete aircraft, validations were 

performed on a simplified geometry. Using a 3D 

wing-body model, predictions of pressure and lift 

coefficient were compared to experimental data, 

and very good agreement was obtained. These 

results gave the engineers confidence in CFD, and 

its use has expanded to include many components 

and aspects of the full-scale aircraft. 

As an example, the flow above the joint that is 

formed between the wing and body of an aircraft has 

been examined. The flow in this region can separate, 

and if it is possible to reduce or eliminate the size of 

the separation region by design modifications, the 

flight performance of the plane can be improved. 

Engineers at ACAC have used FLUENT to study this 

phenomenon and improve the flow characteristics. 

Pressure (Form) Drag Friction Drag Total Drag 

Clean wing 0.01547 0.00412 0.01959 

Wing with a rear-mounted nacelle 0.01367 0.00397 0.01764 

As another example, a rear-mounted nacelle configuration 

has been studied, in which the aircraft’s 

engines are installed behind the wings. In this configuration, 

the position of the nacelle or the design 

of the wing can cause the wake of the wing to be 

entrained by the engine, compromising its performance. 

Using FLUENT, the pressure distribution on 

the wing for several different rear-mounted nacelle 

positions has been computed for a flight Mach 

number of 0.78 at an altitude of 35,000 feet. The 

results showed that the lift coefficient of the wing 

with this configuration is lower than that of a clean 

wing (without the interference of the nacelle). The 

drag coefficient is also lower, however, so that the 

lift/drag ratio is increased. Furthermore, the interference 

caused by rear-mounted nacelles makes the 

nose-down pitching moment decrease and it can 

reduce the trim drag produced by the elevators. The 

overall analysis of drag indicates that the existence 

of a rear-mounted nacelle will have little influence 

on friction drag, but can have the advantage of 

reducing the pressure drag. 

The influence of a rear-mounted engine nacelle on components of drag for a case with a wing lift coefficient of 0.46 

CP 

-1.5 

-1.0 

-0.5 

experiment 

FLUENT 

Z = 0.75 

Z = 0.37 

0.0 

0.5 

Static pressure on the 

surface of the ARJ21 

0.0 

0.2 0.4 0.6 0.8 1.0 

x 

A comparison of the pressure coefficient computed 

by FLUENT and measured in the wind tunnel for 

two wing span locations for the simplified wing-body 

geometry 

Flow separation near the wing-body joint (lower left) 

was improved by using CFD to alter the design 

(lower right) 


AEROSPACE 

Compressors Benefit from 

the 

NASA Rotor 37 

By Răzvan Mahu and Cristina Oprea, TENSOR, Bucharest, Romania 

percent span 

100 

80 

60 

40 

20 

0 

1.24 

experiment 

FLUENT 

1.28 1.32 1.90 2.10 2.30 

total pressure ratio 

The total temperature ratio (left) and total 

pressure ratio (right) along the wing span, 

ranging from the blade root (0%) to the blade 

tip (100%), approximately 10cm from the 

leading edge at 98.7% of the maximum 

(experimentally measured) flow rate (20.93kg/s) 

2.2 

2.1 

2.0 

1.9 

19.0 

experiment 

FLUENT 

19.5 20.0 20.5 21.0 

mass flow (kg/sec) 

The predicted compression 

ratio is within 1% of the 

experimental values 

throughout the range of 

mass flow rates studied 

THE NASA ROTOR 37 is among the most popular rotors that have been manufactured 

to date. It was designed and tested in the early 1980s by Reid and 

Moore at NASA Glenn Research Center [1]. It consists of 36 blades with multiple 

arc profiles, and was designed for compressors and turbines with a compression 

ratio of 2.05 at a mass flow rate of 20.19 kg/s. 

Steady-state numerical simulations of the NASA Rotor 37 have been performed 

using FLUENT for one operating curve. Using a constant rotational speed (17,188 

rpm), the simulations covered a range that extends from the maximum flow rate 

to the pumping regime. A fully structured mesh of 420,000 cells was built using 

GTurbo, Fluent’s turbomachinery preprocessor. During the preliminary runs, the 

shock wave accuracy was found to strongly depend on the mesh resolution 

around the blade. Consequently, most of the cells were concentrated near and 

between the blades. A comparison of turbulence models was also conducted 

early on, and the most suitable one for this case was found to be the realizable 

k-ε model. Since the mean y + value was around 33, non-equilibrium wall 

functions were used. An axial flow direction was set at the inlet, and rotationally 

periodic lateral boundaries were used. An inlet temperature of 288.16K was 

assumed, and the exit pressure was altered for each of seven points on the 

operating curve that were simulated. 

Using data from published experimental results [2] a comparison of the radial 

variation of the pitch-averaged total pressure ratio was made, 10.19 cm from the 

blade leading edge. The results indicate that the total pressure is lower at the 

blade tip (100% span) resulting in losses due to the tip gap. A comparison of the 

total temperature ratio along the blade span, measured at the same location, 

shows an increase in the total temperature at the blade tip due to the friction 

between the fluid and the shroud, and to the detaching of the boundary layer. 

In both cases, the FLUENT results were in very good agreement with the data. 

Contours of Mach number at 70% of the blade height 

at 98.7% of the maximum experimental flow rate 

Predictions of the efficiency of the compression process along the operating 

curve were found to be good at low flow rates, but to deviate from the data at 

large flow rates. The total temperature ratio along the operating curve was found 

to be in good agreement throughout the range, even though the computed 

values were higher. A comparison of the compression ratio as a function of the 

flow rate was found to be within 1% throughout the range studied. 

Contours of Mach number on a plane through the blades illustrate some of the 

characteristics of flow in transonic compressors. The detached bow shock wave 

at the leading edge of each blade generates a normal shock wave in the flow 

channel that is the main compression mechanism for this type of compressor. 

There is a low momentum fluid region behind the shock wave, which is the 

second main source of total pressure loss and compression efficiency. 

References 

1 Reid, L. and Moore, D.: Performance of Single-Stage, Axial-Flow Transonic Compressor 

With Rotor and Stator Aspect Ratios of 1.19 and 1.26 Respectively, and with Design 

Pressure Ratio of 2.05. Tech Rep. TP-1338, NASA. 

2 NASA/TM-2003-212457 Report, http://gltrs.grc.nasa.gov 

Pathlines colored by relative Mach number at 98.7% 

of the maximum experimental flow rate 


FOOD 

Image-based 

Easy as Pie 

Meshing: 

By Emma Johnson, Roderick Ross and Philippe Young, Simpleware Ltd., Exeter, UK; Gavin Tabor, University of Exeter, Exeter, UK 

IMAGED-BASED MESHING is opening up exciting new possibilities for the 

application of computational continuum mechanics (numerical methods such as CFD 

and finite element analysis (FEA)) to problems in biomechanics, biofluid dynamics and 

materials characterization. Software solutions that can rapidly generate robust, high 

quality meshes from complex 3D image data, as can be obtained from magnetic resonance 

imaging (MRI), computed tomography (CT) or ultrasound for example, are 

increasingly in demand. Such software improves productivity, leads to significantly 

more accurate results and enables engineers to focus on the analysis and generation 

of results rather than on the geometry definition and mesh creation. 

The University of Exeter and Simpleware Ltd. have collaborated to illustrate the 

potential of new meshing techniques by generating a CFD model of a hot mince pie, 

cooling in a light breeze. The transient study uses Simpleware software to create a 

mesh from medical scan data and FLUENT to analyze the coupled heat transfer and 

fluid flow of the system. The example illustrates a number of more general points 

pertaining to multi-physics simulations on complex image-based domains. First, the 

method allows watertight surface meshes and fully volumetric meshes of complex 

structures to be generated. Second, the accuracy of the geometric reconstruction is 

only dependent on the image quality, which is a function of the scan resolution, noise, 

and contrast between the volumes of interest. In fact, because structures are defined 

by iso-surfaces that are interpolated across boundaries, the geometric reconstruction 

can be of sub-voxel accuracy. (A voxel is a volume pixel, or smallest volume for which 

image data can be stored.) Third, the method allows multiple structures to be 

identified and meshed, with perfectly conforming/non-overlapping boundaries, and 

with the option of defining contact surfaces between them. Finally material properties 

within a given structure can be assigned based on signal strength. In the pie example, 

thermal properties could be varied throughout the mesh of the crust based on fat 

content, for example, and the different regions could then be modeled as separate 

solid zones. 

The segmentation of structures (crust, filling, raisins and air) in 

ScanIP software 

A 60g mince pie was scanned using MRI. Four segmentation masks, representing the 

crust, filling, raisins, and air, were generated using Simpleware’s ScanIP segmentation 

software. A volumetric mesh was then generated in the +ScanFE mesh generation 

module and exported directly to FLUENT. The time required to go from the import of 

the 3D data through to the creation of the multi-part input mesh for FLUENT was less 

than 10 minutes on a PC. 

In FLUENT the physical properties were assigned and the filler temperature was set to 

450K. A rectangular space was created around the pie with boundaries 30cm downstream 

and 4cm on either side of the pie. Upstream of the pie, an inlet condition was 

set with a velocity of 2m/s and temperature of 300K, akin to a short term cooling of 

the pie in a breeze. These boundary conditions were used for a transient calculation 

during which the filler was allowed to cool naturally for a time of 300s (5 minutes). 

The results show features such as the temperature gradients inside the pie and the 

recirculation zone downstream of the pie and inlet. 

Temperature contours and surfaces inside the pie, and 

velocity vectors showing the cooling breeze at the start of 

the calculation (top) and after several minutes (bottom) 

More.info@ 

www.simpleware.com 


FOOD 

Looking Inside 

Dough Mixers 

By Robin Connelly, Departments of Food Science and Biological Systems Engineering, University of 

Wisconsin, Madison, Wisconsin, USA, and Jozef Kokini, Department of Food Science, Center for Advanced 

Food Technology, Rutgers University, New Brunswick, New Jersey, USA 

View of the C.W. Brabender, Inc. Farinograph ® twin 

sigma blade mixer 

Mixing Index for Newtonian corn syrup for two 

mixer positions, where a value of 0 (blue) indicates 

pure rotation, 0.5 indicates simple shear, and 1 (red) 

indicates pure elongation 


WHEAT FLOUR DOUGH is a rheologically 

complex viscoelastic material, whose unique timedependent 

properties are governed by the rate, 

amount, and type of deformation applied. The 

structure and morphology of dough also depend on 

the available moisture and the extent to which the 

dough is mixed. As a result, dough is a very dynamic 

and unstable material, and dough mixers have 

evolved into highly complex geometries that shear, 

stretch, and fold. In addition, they often have close 

wall clearances to ensure that there are no regions 

of ineffective mixing. Changing between mixer 

types, especially between batch and continuous 

mixers, is difficult because of the very different flow, 

shear, extension, and mixing profiles that characterize 

each one. In industry, determining mixing times 

and designing mixer configurations is largely done 

on a trial and error basis. 

A more predictive approach is to calculate measures 

of mixing effectiveness through the use of mathematical 

modeling and numerical simulation. An 

ongoing project at the Center for Advanced Food 

Technology at Rutgers, The State University of New 

Jersey, supported by the National Research Initiative 

of the USDA Cooperative State Research, Education 

and Extension Service*, is using POLYFLOW to study 

the flow and mixing in typical dough mixer geometries 

with fluid models that range from simple 

Newtonian to non-linear viscoelastic [1,2]. The work 

featured here involves the numerical simulation of 

the flow and mixing in a fully filled Farinograph ® 

mixer using the mesh superposition technique and 

particle tracking with a Newtonian fluid model 

based on a high viscosity corn syrup [3]. Future 

work with this geometry will focus on extending the 

simulations to non-Newtonian fluid models. The 

Farinograph is a low shear rate batch mixer with 

two non-intermeshing, asymmetrical sigma blades, 

where the fast (right) blade turns at 93 rpm counterclockwise 

and the slow (left) blade turns at 62 

rpm clockwise. CAD STEP representations of the 

blade geometries were provided by C.W. Brabender 

Instruments, Inc., South Hackensack, New Jersey, 

producer of the Farinograph Mixer. 

Because the blades turn at different speeds, two 

revolutions of the slow blade and three revolutions 

of the fast blade are required before there is repetition 

of the relative blade positions. The left (slow) 

blade mesh of 6232 tetrahedral elements and the 

right (fast) blade mesh of 6166 elements are superimposed 

on the bowl (41860 hexahedral elements) 

every 0.027 seconds, giving a total of 72 positions 

per blade cycle with 10° between positions for the 

slow blade and 15° between positions for the fast 

blade. The time marching flow simulation results are 

then used to generate particle tracking data for 

10,000 massless material points initially randomly 

distributed throughout the flow domain, or a set of 

1000 massless material points initially randomly 

distributed in a 1 cm 3 box. As these abstract points 

are tracked throughout the flow domain, the associated 

local flow characteristics are recorded, thus 

providing a spatial and temporal history of phenomena 

such as stretching and deformation. Random 

distribution is a requirement of the statistical mixing 

measures that are used to evaluate the particle 

tracking results. 

The simulation results show that the differential in 

the speed of the two blades in the Farinograph 

causes an exchange of material between the blades 

to occur. The primary circulation pattern consists of 

material moving from the slow blade up toward the 

top of the mixer and over toward the fast blade, 

while the fast blade pushes material towards the 

slow blade near the bottom of the mixer. A slower 

mixing pattern is also observed where material 

around the blades moves from the center towards 

the walls and then up towards the top and back 

down in the center of the mixer. The zone in the

FOOD 

center of the mixer between the two blades is 

shown to have excellent distributive and dispersive 

mixing ability with high shear rates and mixing 

index values [4]. The mixing index is a measure of 

the type of flow, with values that range from 0 for 

pure rotational flow to 0.5 for shear flow to 1 for 

pure elongational flow. A high value of the mixing 

index combined with a high shear stress and shear 

rate indicate an area in the mixer with potentially 

good dispersive mixing capability. That region also 

has fast distribution throughout both sides of the 

lower section of the mixer as shown by material 

point clusters that travel through it. In contrast, very 

slow mixing is seen in the area away from the region 

swept by the blades that is generally not filled 

during normal use of this mixer. 

The mean of the normalized length of stretch [5] 

calculated for material points in the Newtonian fluid 

case increased exponentially over time, indicating 

effective mixing for the majority of material points. 

In the area swept by the blades, the material points 

with the highest values of the length of stretch are 

generally located near the blade edges or in the area 

swept by the blade edges. However, material points 

with high length of stretch values are also found 

outside these zones in a more random distribution. 

The instantaneous efficiency, which can be thought 

of as the fraction of energy dissipated locally that is 

used to stretch a fluid element at a given instant in 

a purely viscous fluid [5], gives a picture of the most 

and least effective blade positions for applying energy 

to stretch rather than displace material points. 

The least effective blade positions are when the flattened 

central sections of both blades are horizontal, 

while the most effective mixing occurs when the 

flattened section of the fast blade is vertical. The 

mean time-averaged-efficiency [5] stays above 

zero while its standard deviation reduces over time, 

indicating that the majority of the points are 

continuously experiencing stretching at equivalent 

levels over time. 

The overall result of this project has been to 

demonstrate the effectiveness of numerical simulation 

as a means to non-intrusively study the 

flow and mixing in a given mixer of materials 

with different rheological properties. The results 

also have established a basis of comparison 

between mixers with very different geometries. 

The simulations are providing guidance for the 

design of experiments that will be used to 

validate the findings of the simulations. Once the 

simulations are validated, they will be able to 

provide a much higher level of detail than the 

experimental results. The insight gained by this 

research has already stimulated the development 

of ideas by those involved on how the flow and 

mixing in a mixer affects the development of 

material structure, leading to profitable new lines 

of research. 

References: 

1 Connelly, R.K. and Kokini, J.L.: Analysis of Mixing in a 

Model Mixer using 2-D Numerical Simulation of 

Differential Viscoelastic Fluids with Particle Tracking. 

J. Non-Newt. Fluid Mech., 123:1-17, 2004. 

2 Connelly, R.K. and Kokini, J.L.: 2-D Numerical 

Simulation of Differential Viscoelastic Fluids in a Single- 

Screw Continuous Mixer: Application of Viscoelastic 

FEM Methods. Adv. Poly. Tech., 22(1):22-41, 2003. 

3 Connelly, R.K. and Kokini, J.L.: Mixing Simulation of a 

Viscous Newtonian Liquid in a Twin Sigma Blade Mixer. 

AIChE J., In Review, 2006. 

4 Yang, H.-H., Wong, T.H. and Manas-Zloczower, I.: 

Flow Field Analysis of a Banbury Mixer. In: Mixing and 

Compounding of Polymers: Theory and Practice, Ch. 7, 

pp. 187-223, Manas-Zloczower, I. and Tadmor, Z. 

Editors, Carl Hanser Verlag, New York, 1994. 

5 Ottino, J.M.: The Kinematics of Mixing: Stretching, 

Chaos and Transport. Cambridge University Press, 

1989. 

6 Connelly, R.K. and Kokini, J.L.: 3D Numerical 

Simulation of the Flow of Viscous Newtonian and Shear 

Thinning Fluids in a Twin Sigma Blade Mixer. Adv. Poly. 

Tech., In Review, 2006. 

* Grant numbers 2001-35503-10127 & 2003-35503- 

13907. 

Newtonian corn syrup fluid velocity vectors for blade 

positions of 180° and 270° on the z=0 (top), x=0 

(middle), and y=4.225 cm (bottom) planes; the vectors 

are all the same length and colored by the velocity 

magnitude 

Initial (left) and final (right) positions after 3 blade cycles (6 revolutions of the slow blade and 9 revolutions of the 

fast blade) of 1,000 material points in the Newtonian corn syrup, colored according to the length of stretch [6] 

For the Newtonian corn syrup, 3D positions of 

10,000 initially randomly distributed material points 

(top) and after three cycles (bottom) with 

concentrations of 1 (red) and 0 (blue) [6] 


HEALTHCARE 

CFD Assists Neonatal 

By Maciej K. Ginalski and Andrzej J. Nowak, Institute of Thermal Technology, Silesian University of Technology, Gliwice, Poland 

Pathlines, colored by temperature, illustrated the 

circulation inside the left side of the incubator 

Postprocessing courtesy of Michal / Nowak 

PREMATURE INFANTS generally enter the world with little protection from 

the harsh environment. Thermal comfort thus plays a crucial role in their survival 

and health. To provide the optimal environmental conditions for these infants, 

incubators are widely used. However, due to the complexity of the 

physical processes occurring within modern neonatal units during treatment 

procedures, comprehensive analysis involving the thermal comfort of premature 

infants can be difficult. The application of analytical calculations is currently not 

practical, and therefore numerical modeling is required. 

At the Silesian University of Technology in Gliwice, Poland, a CFD model of 

conjugate fluid flow and heat transfer in an infant incubator has been developed 

to support infant healthcare and improve medical equipment design. Accurate 

geometrical models representing the human body in a variety of postures are 

now easily created with high accuracy. Fluid flow analysis can be performed by 

taking into account mechanisms of heat transfer, such as radiation, convection, 

conduction, and even the evaporation of sweat and moisture from human skin. 

Since all infants, and particularly premature infants, are different from each other, 

the complex shape of the human body was generated using the CATIA package 

and described by a number of scaleable parameters. By changing the value of 

those parameters, a geometrical model can easily be adjusted to the individual 


HEALTHCARE 

Intensive Care 

features of each analyzed patient or to his nursing position. The researchers used 

TGrid 4.0 to wrap the surface model of the infant’s body and to create the surface 

mesh to be exported to GAMBIT. In parallel, they created the geometry of 

the incubator using the CATIA package and combined the baby and incubator 

geometries using GAMBIT. 

Once a fine tetrahedral volume mesh was created, numerical calculations were performed 

in FLUENT. The research team enhanced the FLUENT model using several 

user-defined functions (UDFs) to control the processes of heat transfer together 

with internal heat generation inside the infant’s body, evaporation of moisture from 

the infant’s skin, and respiration. This last component of heat balance can be modeled 

as a steady-state or transient process. To make the calculation process fully 

automatic, managing software called MARCEL was developed to assist the team at 

all essential stages of the numerical simulation. MARCEL reads the provided data, 

manages the process of geometry and mesh creation, and sets the appropriate 

boundary and initial conditions. After receiving the results from the CFD solver, 

MARCEL then provides a basic report about the heat balance of the infant being 

analyzed. It is anticipated that a modified version of the program could be used by 

medical staff to perform heat balance calculations without possessing any knowledge 

of how to build and run CFD models. 

Contours of velocity magnitude on two planes through the 

incubator; red regions correspond to the flow inlets at the 

ends and outlets at the sides 

Validation of the code has been performed with comparison of the results to a 

series of clinical tests described in the medical literature. In all cases numerical 

models were created to represent analyzed infants and environmental conditions 

measured in the described tests. Results from the numerical simulations were 

compared with those obtained from the literature and proved to be very accurate. 

Researchers also performed a series of air temperature and velocity measurements 

inside an empty prototype of a new design. These measurements were 

fairly consistent with the results obtained from the numerical simulations. Based 

on this conclusion, modification of the ventilation system has been proposed and 

verified numerically. 

The incubator modeling project is still in the phase of testing and validation. 

However, even at the present stage, it is providing crucial information and is 

becoming an important tool in the treatment of premature babies at neonatal 

intensive care units. 

Suggested Reading: 

Ginalski, M., Nowak, A.J. and Wrobel, L.C.: Computational Model of Selected Transport 

Processes of the Premature Newborn Baby within an Infant Incubator. XXI International 

Congress of Theoretical and Applied Mechanics, Conference proceedings, Warsaw, 

Poland, August 2004. 

Ginalski, M., Nowak, A.J. and Wrobel, L.C.: Combined Heat and Fluid Flow in a Double 

Wall Infant Incubator. International Conference of Computational Methods in Sciences 

and Engineering, Conference proceedings, Loutraki, Greece, October 2005. 

Ginalski, M., Nowak, A.J. and Brandt, J.: Numerical Optimization of the Infant 

Incubator Ventilating System. Conference Proceedings, XIX National Congress of 

Thermodynamics, Sopot, Poland, September 2005. 

Acknowledgments 

The authors wish to express their gratitude to Nicolae Mera from the Center for 

Computational Fluid Dynamics at Leeds University, UK; Professor L.C. Wrobel from Brunel 

University, UK; Keith Hanna from Fluent Europe; and Michal / Nowak for their contribution 

and help in the project. 

Temperature contours show a uniform field in the region 

surrounding the infant 

Velocity vectors without (left) and with (right) an overhead plastic screen; 

the screen was found to help reduce radiative heat loss from the child 


ENVIRONMENTAL 

Activated Sludge Basins 

Get on Track 

By Gregory Cartland Glover, Karim Essemiani and Jens Meinhold, Veolia Environment R&D Center, Maisons Laffitte, France, 

and Stephanie Vermande, Technical Division of Veolia Water, St. Maurice, France 

Hydrodynamic phenomena in 

the ps-ASB, showing gas fraction 

iso-surfaces (top) and pathlines 

of liquid velocity (bottom) 

concentration (mg/l) 

25 

20 

15 

10 

5 

0 

0 

Feed Aerobic Reaction 

experiment 

FLUENT ASM1 Anoxic Reaction 

WEST ASM1 

Ammonia 

Oxygen 

Nitrate 

30 60 90 120 150 180 210 240 270 

time (min) 

Profile plots of the reaction phenomena in the ps-ASB 

ACTIVATED SLUDGE BASINS (ASBs) 

are a key step in the treatment of municipal 

wastewater. ASBs are used to degrade 

biochemically reactive pollutants from 

water that is discharged into the natural 

environment. The bacteria found in the 

sludge consume and assimilate nutrients 

such as carbon, nitrogen, and phosphorous 

under several environmental conditions. 

The design of ASBs is difficult, 

however, due to factors including: 

• the treatment aims (pollutants 

to be degraded) 

• the influent wastewater composition 

(time and location 

dependent) 

• the application of liquid agitation 

and aeration (brushes, 

disks, membrane aerators, 

impellers, turbines and venturi 

tube mixers) 

• the location and spatial 

requirements (local weather, 

above or below ground, 

sparsely or densely populated 

regions) 

• the daily treatment loading 

(from a few hundred liters to 

thousands of cubic meters) 

Each one of these factors influences the 

size, form, and mode of operation (continuous 

or sequenced batch treatment) that 

is used when designing an ASB. Variation 

in some of the factors, such as daily 

changes in the temperature and in the 

influent composition and flow rate, can 

impact the efficacy of the treatment 

process. Thus, for the design of any ASB, 

an appropriate selection of agitators and 

aerators is essential to provide optimal 

environmental conditions for the treatment 

of wastewater. 

A study that was co-funded by Anjou 

Recherche, the R&D arm of Veolia Water, 

and the European Union was recently performed 

to develop a computational strategy 

for modeling ASBs. The primary focus 

of the study was to examine the effect of 

the local hydrodynamics on the biochemical 

reactions observed in ASBs for both 

pilot and real scale processes. The results 

provided an improved understanding of 

the hydrodynamic impact on the environmental 

conditions experienced by the 

bacteria. 

A pilot-scale ASB (ps-ASB) was used to 

establish a database of hydrodynamic data 

(circulation velocity and mean bubble 

sizes) and interphase mass transfer data 

under different operating conditions. The 

ps-ASB was then used to study the impact 

of the normal and abnormal operating 

conditions on the biochemical reaction 

phenomena by monitoring the change 

in concentration of key wastewater 

constituents, such as ammonia, organic 

carbon, nitrates, organic nitrogen, and 

particulate matter. 

The ps-ASB applied different environmental 

conditions in a programmed sequence. 

The different conditions were affected by 

contacting the mixed liquor, a mixture of 

wastewater and activated sludge, with air 

in the form of fine bubbles, and applying 

a circulation velocity with a marine-type 

impeller. This condition enabled the aerobic 

oxidation of both organic carbon and 

nitrogen (in the form of ammonia) to carbon 

dioxide and nitrate, respectively. A 

second condition only agitated the biomass 

in the mixed liquor to reduce the 

overall nitrogen content of the wastewater. 

Under these conditions a different 

type of biomass (anoxic bacteria) consumes 

the nitrate producing nitrogen, 

which then evolves as a gas. 

The operational data was used to create a 


ENVIRONMENTAL 

hydrodynamic and biochemical reaction model of 

the ps-ASB. The first step was to model the biochemical 

reactions assuming perfectly mixed reactor 

conditions with the wastewater modeling tool 

WEST ® [1], which uses the Activated Sludge Model 

No. 1 (ASM1) protocol [2]. 

The mixed liquor composition and the calibrated 

kinetic and stoichiometric parameters derived from 

this global model were then applied to a converged 

local hydrodynamic solution constructed in FLUENT. 

The Eulerian multiphase model was employed with 

the dispersed phase form of the k-ε turbulence 

model to resolve the gas-liquid motion under steady 

flow conditions. 

The application of the ASM1 protocol to FLUENT 

was validated by analysis of the component profiles 

during the aerobic and anoxic reaction phases. The 

rates under the different operating conditions were 

accurate to within 10% of the experimentally measured 

reaction rates, where the accepted error was 

10 to 15%. 

Liquid velocity pathlines and volume fraction contours 

of the ps-ASB were used to illustrate the 

hydrodynamics of the basin. The distribution of dissolved 

oxygen after one hour of aeration was also 

examined. The difference between the cold and 

warm iso-surfaces was found to be 0.4 mg/l over 

the range from 2.9 to 3.4 mg/l. At this point in the 

simulation, the oxygen concentration gradient does 

not influence which biochemical reaction processes 

are predominant. However, at a mean concentration 

of less than 0.5 mg/l different treatment 

regimes could be found in the reactor (informal 

aerobic and anoxic zones). 

The aeration and agitation regimes applied to the 

ps-ASB were considered to be perfectly mixed and at 

a comparatively small scale for treatment processes. 

Thus, the effects observed with the ps-ASB may be 

different from those of real scale Activated Sludge 

Basins (rs-ASBs) where volumes of up to 10,000m 3 

are widely used. At the larger scale, perfectly mixed 

reactor or plug flow regimes are not guaranteed, 

and the potential influence of the mixing regimes is 

far greater and more difficult to categorize. Thus, 

incorrect operating conditions will cause mixing 

regimes that would hinder the ability of the ASB to 

meet the treatment objectives. 

To test this possibility, two rs-ASBs with different 

geometries and hydrodynamic regimes were simulated. 

The basin rs1-ASB is a carrousel type ASB, whereas 

rs2-ASB is a race-track type ASB. The information 

derived from these simulations (circulation velocity 

and oxygen mass transfer characteristics) indicated 

how well each ASB could meet its objectives. 

The pathlines for rs1-ASB indicate that the conditions 

are near optimal, while pathlines in rs2-ASB 

showed that the process was not close to optimal 

conditions. The flow field in rs2-ASB was characterized 

by low horizontal liquid velocities and large 

recirculation zones and short-circuiting upstream of 

the bubble plumes. The liquid circulation patterns 

were disrupted by the bubble plumes, causing them 

to degrade. In general, poor liquid circulation 

reduces the oxygen mass transfer rates and the dispersion 

of oxygen throughout the ASB. The ability 

of the biomass in this particular basin to aerobically 

treat the wastewater could therefore be limited by 

the operating conditions applied. 

Further simulations were performed to improve the 

aeration strategy by analyzing the effect that different 

aerator and agitator configurations had on these 

two design parameters. The simulations examined 

the positions of the aerators and agitators as well as 

the agitation rate and size of the impellers, with the 

single goal of improving the mass transfer characteristics 

of rs2-ASB. The hydrodynamics of the modified 

design showed improved liquid circulation 

with less disruption of the flow by the bubble 

plumes, resulting in a 250% increase of the circulation 

velocity. 

The computational strategies developed during the 

course of this study have enabled Veolia Water 

to obtain a numerical tool that can aide decisions 

made in selecting aeration and agitation configurations 

for Activated Sludge Basins, for the design of new 

technologies and retrofitting of existing installations. 

References: 

1 Hemmis, N.V., WEST ® , Hemmis N.V., Kortrijk, Belgium, 

2003. 

2 Henze, M., Gujer, W., Mino, T., and van Loosdrecht, M.: 

Scientific and Technical Report No. 9. IWA Publishing, 

London, UK, 2000. 

rs1-ASB rs2-ASB Modified rs2-ASB 

Hydrodynamic phenomena in the rs-ASB, showing gas fraction iso-surfaces (top) and pathlines of liquid velocity (bottom) 


ACADEMIC NEWS 

A Dry Passage 

to the Afterlife 

By Essam E. Khalil and Omar A.A. AbdelAziz, 

Cairo University, Egypt 

EGYPT WAS ONCE HOME to the Pharaohs, 

one of the oldest and most sophisticated civilizations 

in the ancient world. Many Egyptian artifacts, 

treasures, and buildings are a priceless part of our 

world heritage. The Valley of the Kings at Luxor is 

a unique and world famous site where thousands 

of years ago, many Egyptian monarchs had elaborate 

tombs built to ensure their safe passage into 

the afterlife. Many of these tombs were robbed 

after being discovered, but others have survived 

and their greatest remaining treasures are the 

beautiful, vivid, yet fragile wall paintings and decorations 

that lie within. Each year, tourists flock to 

Egypt to see these awe-inspiring tombs, but their 

visits cause a major problem for curators. The heat 

and humidity given off by visitors in the enclosed 

chambers and passageways cause damage to the 

plasterwork and paintings. Hence, the ventilation 

system and resultant air flow patterns in a tomb 

are critical to the preservation of the exhibits so 

that they may be kept open to tourists for many 

years to come. 

The Egyptian government and the Supreme 

Council of Antiquities approached ventilation 

experts at Cairo University for help in solving this 

problem for the archeaological tombs in the Valley 

of the Kings. The engineers were asked to devise a 

climate control system for these tombs; a pilot 

study was conducted on the tomb of Ramses VII. 

Using GAMBIT, a model of approximately one 

million cells was created that yielded good geometric 

representation of the tomb passageway 

with the sarcophagus in the main room and a 

large number of visitors. They then used custom 

models in FLUENT to simulate human breathing 

and heat generation, and considered a worst case 

scenario when 20 adult visitors are in the tomb at 

the same time. Parametric CFD simulations were 

performed to predict what the relative humidity, a 

key variable, would be like near the wall paintings 

for a given air extraction system design and tomb 

vent locations. Their modeling work was used to 

determine the optimized air flow pattern for the 

tomb. Based on their findings, the installation of 

raised flow exhausts that were unobtrusive to 

visitors was recommended. The tomb now has 

minimal adverse flow and humidity gradients 

within the chamber, thereby helping to preserve 

this priceless piece of history for future generations 

of visitors. 

Predicted local humidity levels on a plane near the 

main wall paintings of the tomb of Ramses VII with a 

representative adult male tourist figure; the 

sarcophogus is in the foreground 

Postprocessing courtesy of Maciej Ginalski 

Convective 

By Daniele Melideo, Davide Mazzini, Enrico 

A NUMBER OF BENEFITS have resulted from 

recent reductions in weight and improvements to 

the efficiency of aeronautical gearboxes. Reduced 

heat generation inside the gearbox means that less 

oil is needed to maintain a certain transmission 

operating temperature. In addition, reductions in 

surge system losses allow the lubrication system 

components, such as tanks, high pressure tubes, 

filters, and pumps to be smaller in size. 


Predicted pathlines colored by moisture 

in the air in the tomb of Ramses VII; 

the flow is from left to right 

Postprocessing courtesy of Maciej Ginalski 

In the framework of thesis work carried out in the 

Department of Mechanics, Nuclear and Production 

Engineering (DIMNP) of the University of Pisa, the 

convective motion of the air inside a gearbox 

induced by the rotation of the gear drive components 

has been investigated. FLUENT was used for 

the numerical modeling component of the project, 

which focused on a single gear pair. The reference 

equipment is located at the Research Center on

ACADEMIC NEWS 

Student Submariners Peddle 

Their Way to Victory 

By Keith Hanna, Fluent News and Joost Sterenborg, Technical University Delft, Netherlands 

MANY UNIQUE CFD applications 

come across the desk of Fluent 

News, and one that recently appeared 

was of a human powered submarine. 

Students from university engineering 

teams around the world have come 

together for an international humanpowered 

submarine competition for 

the last eight years. The teams of student 

engineers have to design their 

own submarines from the ground up 

using modern computer-based tools, 

and manufacture and test their 

designs for performance and safety. 

They also need to secure enough 

sponsorship funding to cover the cost 

of their team, including travel to the 

competition venue. 

WASUB, an intrepid team from the 

Technical University of Delft in the 

Netherlands approached Fluent 

Benelux, along with other companies 

in the region, for sponsorship last 

year. They used Pro/ENGINEER ® 

from PTC to design their submarine 

in CAD, and FLUENT to hone the 

hull and fin shapes for optimal hydrodynamic 

drag characteristics. After 

extensive testing of the WASUB 

in the MARIN towing tank in 

Wageningen, the craft was shipped 

to America for the races. The team 

came away with several prestigious 

prizes, including best design and 

fastest in two categories. Throughout 

the process, the young engineers 

gained modern product design engineering 

experience in a competitive 

situation. Fluent salutes these brave 

Dutch submariners and their revolutionary 

WASUB design! 

More.info@ 

http://wasub.oli.tudelft.nl 

The WASUB Team in Maryland, June 2005 

CAD layout of WASUB for a single 

human-powered propulsion system 

Axisymmetric FLUENT prediction of static 

pressure around the WASUB hull shape 

Motions Inside a Gearbox 

Manfredi, and Maria Vittoria Salvetti, The University of Pisa, Italy 

Advanced Technology Mechanical Transmissions 

(CRTM) of DIMNP, as part of a collaboration with 

the Italian aerospace company Avio. The test section 

consists of two rotating shafts that support the test 

gears. Wheel lubrication is ensured by two spray 

bars injecting oil upstream and downstream of the 

mesh point, where the gear wheels come together. 

The first activity conducted was to test different 

turbulence models (Spalart-Allmaras, k-ε, k-ω, and 

Reynolds stress). Their performance was evaluated 

by studying 2D cases of Couette motion, consisting 

of two coaxial cylinders rotating at different velocities. 

The k-ω model was chosen as the most costeffective 

turbulence model, since it was able to 

capture the swirling velocity component better than 

the other RANS models, and did not require the 

additional computational resources of RSM. This 

model was used for subsequent numerical studies. 

The 3D simulation of the gearbox focused on the air 

motion induced by the wheel rotation, and included 

the effects of protruding hardware, such as bolt 

heads. Owing to a plane of symmetry, half of the 

gearbox was modeled. The oil presence was neglected 

in the first round of simulations. The wheel 

rotation was simulated using the multiple reference 

frames (MRF) model. The gear teeth, small on the 

scale of the wheels, were neglected. The bolt heads 

were found to entrain the air creating a venting 

effect. While the wheel rotation produced mostly 

circumferential flow, a significant axial velocity was 

observed between the spray bars and the mesh 

point, suggesting a possible deflection of the oil jet. 

In the next stage of the work, a more refined grid 

and the use of the sliding mesh model will be 

adopted, and the oil presence will be taken into 

account. 

Axial velocity on the symmetry 

plane, with vectors showing the 

axial flow near the mesh point 


PRODUCT NEWS 

Fluent & Microsoft Team to Deliver 

64-bit FLUENT on Windows Clusters 

By Diana Collier, Barbara Hutchings, and Rongguang Jia, Fluent Inc. 

ing the way for cluster-based FLUENT simulations for Windows 

users. With this 64-bit support, Windows users will now be able to run 

much larger simulations than currently feasible and they will also see 

performance improvements due to the enhanced memory management 

and wider memory bandwidth available with 64-bit processors. 

This good news for Windows users is the result of close collaboration 

between Fluent and Microsoft. With excellent technical support from 

Microsoft, Fluent’s development team has optimized FLUENT 6.3 on 

CCS. The resulting cluster solution uses the Microsoft MPI (Message 

Passing Interface) software layer for data communication between 

processors on the cluster, and supports a variety of interconnect 

options including Gigabit Ethernet (GigE), Infiniband, and Myrinet. 

FLUENT 6.3 also takes advantage of the Microsoft job scheduler that 

ships with CCS, providing an off-the-shelf solution for launching and 

controlling jobs on the cluster. 

Water ski jumper simulation showcased on the 64-bit Windows cluster at SC05 

Courtesy of Sports Engineering Group, Sheffield Hallam University; postprocessed using EnSight from CEI 

speedup 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

0 

FL5L1 

FL5L2 

FL5L3 

Linear 

2 4 6 8 10 

number of processors 

Parallel performance of 64-bit FLUENT running the 

FL5L1, FL5L2, and FL5L3 large benchmark cases, 

using Windows CCS 2003 on 3.4 GHz Intel EM64T 

dual-CPU nodes connected with gigE 

30% 

28.5% 

25% 

22.0% 

20% 

16.7% 

14.3% 

15% 

11.8% 

12.5% 

15.3% 

15.7% 

10% 

5% 

0% 

15s1 15s2 fl5s3 fl5m1 15m2 15m3 fl5l1 fl5l2 

performance boost 

Serial performance boost with the 64-bit solution, 

relative to 32-bit, for the small (FL5S1 and FL5S2), 

medium (FL5M1, FL5M2, and FL5M3), and large 

(FL5L1 and FL5L2) benchmark cases 

THE AVAILABILITY OF 64-BIT 

off-the-shelf computing from 

Intel and AMD has provided 

great value to Fluent customers 

seeking to run larger, more 

memory intensive CFD simulations. 

With the upcoming 

release of FLUENT 6.3, 64-bit 

will become an option to customers 

running the Microsoft ® 

Windows ® operating system. 

Many FLUENT customers are 

already running a 32-bit version 

of FLUENT on the 64-bit 

Windows XP operating system, 

but in this configuration FLUENT 

does not take advantage of 

extended memory addressing. 

With FLUENT 6.3, full 64-bit 

capability will be supported on 

both desktop and server systems 

running Windows. On the server 

side, FLUENT 6.3 has been ported 

and optimized to run under 

the new Microsoft ® Windows ® 

Compute Cluster Server 2003 

(CCS) operating system, open- 

Benchmarking 

The performance of 64-bit FLUENT under CCS is excellent. Speed 

improvements for serial performance, relative to the 32-bit FLUENT 

version, are in the range of 10-30% for many of the examples in 

Fluent’s standard benchmark problem set. Parallel scaling is also quite 

good. Initial scaling studies using the larger benchmarks on a Windows 

CCS cluster are on par with what is observed on similarly configured 

clusters running Linux. For example, on an 8-CPU cluster connected 

with GigE, a speed-up of 6.4 was obtained using the FL5L2 benchmark. 

Supercomputing 2005 

Microsoft showcased their new high-performance computing platform 

at Supercomputing 2005 (SC05), held in Seattle November 14-18, 

2005. FLUENT was proud to be part of the Microsoft exhibit, demonstrating 

FLUENT 6.3 running as a 64-bit application under Windows 

Compute Cluster Server 2003 on a 16 CPU cluster provided by 

IBM and using a Myrinet interconnect. The showcased simulation was 

a multi-million cell model of a water ski jumper, courtesy of Sports 

Engineering Group, Sheffield Hallam University. In fact, several Fluent 

partners at SC05 demonstrated FLUENT simulations running on CCS 

clusters, including Dell, Mellanox, Voltaire, and Broadcom. The widespread 

success of these demonstrations, using pre-release versions of 

both the operating system and FLUENT, was a confirmation of 

Microsoft’s stated goal to provide HPC solutions that are easy to 

deploy, operate, and integrate with existing infrastructure and tools. 

For information on obtaining FLUENT 6.3 beta for Windows Compute Cluster 

Server 2003, contact your local Fluent account manager. 

More.info@ 

www.microsoft.com/windowsserver2003/ 

ccs/overview.mspx 


PRODUCT NEWS 

Quick Turnaround with 

Rapid Flow Modeling 

By André Bakker, FloWizard Product Manager, and Laurent Collonge, FLUENT for CATIA V5 Product Manager 

THE OBJECTIVE OF RAPID FLOW MODELING is 

to bring CFD to the broader engineering design 

market, by providing fluid flow analysis tools that let 

engineers quickly turn their CAD models into CFD 

results. There are now two full-fledged rapid flow 

modeling products available in Fluent’s product 

line: FloWizard and FLUENT for CATIA V5. With 

these products, Fluent is taking the lead in what is 

expected to be a fast growing segment of the CFD 

software market. 

Many recent technology advances have made rapid 

flow modeling possible. The increased speed of 

computer hardware has reduced the calculation 

time for many CFD applications to fit within engineering 

design timeframes. A simulation that previously 

might have taken tens of hours on a workstation 

can now be run in well under an hour on a 

standard PC. On the software side, it is now possible 

to embed the CFD experience in highly automated 

products that significantly reduce the turnaround 

time for a fluid flow analysis. 

Modern companies have an increasing need for engineering 

analysis to be tightly integrated into their 

design and PLM (product lifecycle management) 

processes. This requires software that interfaces well 

with CAD and other engineering software tools. It 

also means that the fluids analysis software should be 

accessible to engineering designers, who may have 

only intermittent needs to run simulations. FloWizard 

and FLUENT for CATIA V5 share characteristics that 

allow them to meet these needs. They each have a 

high level of connectivity with CAD or PLM products. 

This is important, since most of their users are engineers 

involved in product design. FloWizard accepts 

a wide variety of CAD and mesh file formats, and 

has tight CAD connections with SolidWorks ® , 

Pro/ENGINEER ® and UGS’ NX. FLUENT for CATIA V5 

embeds the same rapid flow modeling technology 

deep into the CATIA V5 PLM system. 

Both packages focus on modeling well understood 

physics, including compressible and incompressible 

fluid flow and heat transfer, both laminar and turbulent. 

Stationary equipment can be modeled as well 

as rotating machinery. Tasks that often require a lot 

of user interaction, such as geometry cleanup, 

meshing, and solving are fully automated. Not only 

does this save time, it also leads to a significantly 

reduced learning curve. 

Both products make use of the fast, accurate, and 

well-validated FLUENT 6 solver in the background. 

As an additional benefit, the files generated by 

FloWizard and FLUENT for CATIA V5 can be shared 

with FLUENT users as needed. This also helps 

improve the level of collaboration between the 

analysis and design teams. Furthermore, FloWizard 

Starting with a pump model in SolidWorks, engineers can quickly send their 

model to FloWizard. There it will be meshed and solved using FloWizard’s 

automated tools. The final results can be used by the engineer to decide 

upon possible design changes, thus closing the full design cycle. 

offers built-in collaboration tools that let multiple 

users simultaneously connect to the same session 

for design reviews. When there is a need to model 

large problems, both products offer the ability 

to run calculations in parallel on a local or remote 

network. 

In many companies, there is also a need to efficiently 

model recurring problems related to their product 

lines. For this purpose, custom tools can be built 

upon the rapid flow modeling platform. FloWizard is 

fully customizable using the Python programming 

language which has been used by several companies 

to build custom, organization specific analysis 

tools. 


PRODUCT NEWS 

High quality Cooper and tet meshes in a redesigned adaptorconnector 

originating from a Pro/E part file 

Impressive Prepro 

By Erling Eklund, GAMBIT and TGrid Product Manager 

THE SPRING RELEASES of several preprocessing products from Fluent 

promise many exciting new features and capabilities, ranging from CAD 

integration to wrapping technology. 

GAMBIT 

In GAMBIT 2.3 the focus continues to be on CAD import, geometry manipulation, 

and advanced meshing. A new CATIA V5 translator has been added, as well 

as new CAD Connections for SolidWorks ® , Pro/ENGINEER ® , and UGS’ NX. 

These Connections use native surface, connectivity, and topology information, 

and eliminate the need to use a third party generic CAD format to translate your 

geometry model. With the CAD Connections, tools developed by Fluent reside 

in the CAD packages to perform a number of integrity checks prior to export, 

which guarantees that clean CAD integration is achieved. 

All edge and face construction tools in GAMBIT 2.3 can be utilized on these 

CAD geometries including the new closest point projection. In addition, 

design changes and general mesh-to-CAD conversion can be made using a 

new Advanced Covering algorithm. The cleanup tools continue to expand 

with a new sliver face removal capability. 

A surface mesh of occupants 

in a truck cabin, created 

using the new surface 

wrapper in TGrid 

In the area of meshing, the size function capability has been improved 

through speed-ups and added functionality, such as individual minimum 

size assignments, 2D proximity, size function blending, and a new, exciting 

meshed size function, starting on a boundary layer cap. 

For boundary layers, a new first height, last-ratio-based cell growth function 

has been added, and the full 3D boundary layer can be examined prior to 

volume meshing. A new quad split option in tetrahedral meshing allows for 

combined hex and tet meshing, without the need for pyramids, which in 

many cases leads to significant cell quality improvements. 

TGrid 

The most important new feature in TGrid 4.0 is the surface wrapper, which 

allows connectivity, defeaturing, and surface meshing to be handled in a 

single operation. Once the large holes have been covered, the tool wraps 

the geometry with a high-quality surface mesh from which a volume mesh 

can then be created. Gaps, slivers, and overlaps in the geometry are automatically 

cleaned-up in the process. The advanced wrapping procedure 

provides a full spectrum of size controls, including local and global 


PARTNERSHIPS 

Discrete Element 

Modeling of 

Particles for FLUENT 

By Peter Weitzman and John Favier, DEM Solutions, Edinburgh, UK 

The 3Matic-for-Fluent interface, showing all three levels of operations: 

Manual (buttons on top), Autofix (right panel), and Batch Fix (left panel) 

cessing 

proximity, curvature, and zone-based size functions. It also supports 

controlled size growth, and unique coarsening and feature capturing capabilities. 

Other functionalities include the ability to fully examine the region 

to be wrapped, prior to the wrapping procedure, with a guaranteed hole 

finder. There are local and global feature extraction capabilities, automatic 

fixing of incorrect topology, and automatic mesh quality improvement. 

Using a strategy based on an “over-refined” initial mesh, imprinting and 

coarsening together produce the highest-quality surface mesh on the 

market today that is based on wrapper technology. 

In addition to the introduction of the surface wrapper, the prism layer 

technology in TGrid 4.0 has been expanded. The initial normal growth direction 

has been improved, leading to overall speed and quality improvements. 

Two automated functions have been added: one to detect prism layer proximity/collision, 

which includes automatic prism height adjustment, and another 

to detect and peal off prism layers in sharp corners, which includes automatic 

non-conformal re-meshing of prism sides. The ability to handle prism 

layer growth on double-sided, zero-thickness walls has also been added. 

DEM SOLUTIONS is the developer of EDEM, discrete element method 

(DEM) software for particle flow simulation. EDEM is used to simulate the dynamics 

of solid particles including inter-particle and particle-wall collisions. 

Fluent and DEM Solutions have jointly developed a new plug-in for FLUENT that 

can be used to simulate the dynamics of multiphase fluid-particle systems. The 

EDEM plug-in complements the multiphase models already available in FLUENT 

by adding the ability to include particle attributes such as shape and size distribution, 

material properties, and initial placement, and to obtain detailed information 

on collisions, clumping, adhesion to boundary surfaces, and exchange 

with the surrounding fluid. In addition to the fluid drag, other forces such as 

gravity, electrostatic, electromagnetic, bonding, and cohesion can be applied to 

the particles. An open interface allows additional user-defined forces and 

coupling with other physical models. 

The EDEM plug-in can provide important information on multiphase flows across 

a broad range of industries and applications. It is useful in applications where the 

particles must be transported efficiently, without damage or excessive buildup, 

or when particle-equipment interactions are critical. Some examples are the 

optimization of pneumatic transport systems, fluidization, filtration, prevention 

of sand damage, and reactor transport. 

The EDEM software can be used by itself to model single-phase systems of solid 

particles. Some examples of single phase applications include bulk material handling 

in conveyors, chutes and hoppers, rock drilling, excavation, agricultural 

machinery, and various pharmaceutical manufacturing processes. 

A large number of enhancements have also been added in the areas of 

surface mesh intersection, re-meshing, local manipulation, and more. 

3Matic-for-Fluent 

The wrapping of highly complex industrial models often involves dealing 

with hundreds of files, all of different quality and origin. In collaboration 

with Materialise, a new product has been developed to meet this type of 

challenge. 3Matic-for-Fluent 1.0 is designed to handle all types of CAD 

formats (native, standard, and faceted) and produce a coherent, optimal, 

and clean faceted mesh that is ready to wrap in TGrid 4.0. This customized 

tool allows data to be handled in three ways. First, connectivity, cleanup, 

hole filling, and faceting of a single CAD file can be investigated, step-bystep, 

so that the impact of parameter settings can be better understood. 

Second, some of the files can be run through an auto-fix sequence to evaluate 

consistent behavior through different CAD formats. Third, hundreds 

of mixed CAD files can be run through the batch converter using the 

selected parameters, all producing TGrid mesh files. 

3Matic-for-Fluent is sold and distributed by Fluent. Please contact your local Fluent 

office for more information. 

Entrainment of particles in an air-stream 

using FLUENT-EDEM co-simulation 

More.info@ 

www.dem-solutions.com 



Mapping Thermal Data 

from FLUENT to Structural 

Codes Quickly 

By Aleksandra Egelja-Maruszewski, Fluent Inc. 

“Fluent’s add-on UDF for temperature 

mapping is an excellent tool. It allows 

quick and accurate interpolation of 

temperatures between a CFD model 

and a structural mesh. It has cut the 

time and effort it takes us to map 

temperature data from FLUENT to 

FEA drastically. It is simple to use 

and flexible. If you need to map 

temperatures from a CFD model 

to a structural mesh, this is the tool 

to use.” 

– William Towne 

CAE Engineer 

Performance Prediction 

Mercury Marine 

IN MANY MODERN PROCESSES TODAY there is 

an increased need to solve multiphysics problems, 

such as thermal coupling. Some of these problems 

can be solved with one simulation software only; 

others require the combination of multiple software 

packages. In extreme thermal environments, there 

are various types of thermal loads on components 

and structures. Applying these thermal loads correctly 

is one of the first steps required for achieving 

a reliable thermal stress analysis of the component. 

Thermal loads can be applied as a temperature 

distribution within the solid, or as heat transfer 

coefficients on the skin of the solid where the heat 

transfer takes place. 

Most CFD tools have advanced fluid dynamics and 

heat transfer algorithms, but do not have built-in 

advanced solid mechanics analysis capabilities such 

as thermal stress analysis and fatigue prediction. On 

the other hand, most of the advanced finite element 

analysis (FEA) tools have advanced solid mechanics 

algorithms, but do not offer the necessary advanced 

fluid dynamics and heat transfer capabilities to 

determine the correct thermal loads for the thermal 

stress analysis. For scenarios involving fluid flow and 

thermal loading, the two simulation tools become 

dependent on each other. 

To make it easier to perform combined simulations 

such as these, engineers at Fluent have developed a 

user-defined function (UDF) for coupling CFD and 

FEA for thermal stress analysis. With this new capability, 

FLUENT is used to solve for the flow and thermal 

analysis in the fluid and solid zones of a part or 

component. The temperature data of the solid 

region is then supplied to the FE solver as the thermal 

load profile for the thermal stress analysis. 

Because the CFD mesh is typically much finer than 

the FE mesh, it is necessary to interpolate, or map, 

the CFD results to the FE mesh. The mapping is 

accomplished through a Scheme file (read into 

FLUENT) and the UDF. 

For the case of exporting data from FLUENT to 

ABAQUS, for example, the coupled solution would 

proceed as follows. 

1. The user solves the case in FLUENT. 

2. The user reads the Scheme file and 

compiled UDF into FLUENT. 

3. The user reads the ABAQUS ASCII format 

mesh into FLUENT. The FLUENT UDF 

uses import filters to read the nodes, 

elements, and connectivity from the 

ABAQUS mesh. 



The panel used for input to the mesh interpolator 

4. For each FE node/element, the UDF finds 

the closest solution location in the CFD 

mesh. A binary space partitioning (BSP) 

tree is used to find the closest node 

by recursively subdividing the space 

containing the geometry, making fast 

geometric searching possible. 

5. The UDF interpolates from the closest 

node. 

6. The UDF instructs FLUENT to export the 

results in a format suitable for ABAQUS. 

There are two methods for performing this 

mapping procedure: 

• The temperature data inside the solid 

region is exported. For each node in the 

FE mesh of the solid region, temperature 

data will be written to an output file. 

• The temperature and heat transfer coefficient 

at the walls that are solid/fluid 

interfaces are exported. For each surface 

element of the FE mesh, the temperature 

and heat transfer coefficient will be 

written to an output file. 

The UDF is not designed to manage strongly coupled 

CFD and FEA calculations that require many 

iterations back and forth between the solvers. 

Instead, it is designed to make individual data transfers 

easier to do. The UDF, which has panels for 

input, is currently available for ABAQUS, ANSYS, 

and PATRAN formats, but it can easily be 

customized for other structural analysis codes. The 

mesh can be built in millimeters, centimeters, inches, 

or meters. Temperatures can be in Kelvin, 

Fahrenheit, or Celsius. The UDF performs in serial 

and parallel versions of FLUENT. Data for different 

solid zones or wall zones can be exported separately 

by reading the individual mesh files of the zones. 

With additional customization, other properties can 

be exported as well. 

The compiled libraries are freely available for 

exporting steady-state data. Simply download the 

CFD/FEA Thermal Coupling UDF at no charge from 

the UDF examples section of the UDF archive on the 

User Services Center at www.fluentusers.com. (You 

will need your username and password to log on 

to the site.) 

To customize the UDF or obtain libraries for the 

export of transient or time-averaged data, or if you 

have questions regarding the use of this UDF, please 

contact your local Fluent office or log a help 

request through the Online Technical Support 

Portal, accessible from the User Services Center: 

http://clarify.fluent.com/eSupport 

The geometry and mesh of the powerhead of a 

marine outboard is used to illustrate this capability. 

Using the UDF, a 2.8 million cell FLUENT mesh 

was mapped to a 430,000 element ABAQUS mesh 

in 14 seconds. Contour plots of static temperature 

computed by FLUENT (left) and ABAQUS (right) are 

shown for several views of the powerhead assembly 

Courtesy of Mercury Marine 


AROUND FLUENT 

Fluent Opens Larger 

Office in Ann Arbor, MI 

FLUENT HAS OPENED a new, larger office in 

Ann Arbor, MI. This expanded office location allows 

for the addition of more customer service staff, 

an expanded training facility, and a much larger 

computer resource center. The new training facility 

can accommodate more people and allows for the 

addition of new educational sessions on advanced 

automotive application areas such as underhood 

thermal management, noise prediction, and HVAC 

system analysis. 

Upcoming User 

Group Meetings 


Fluent invites you to take full advantage of the 

facility. Meet with the US customer services team, 

get trained on new product innovations, and 

discuss how the Ann Arbor team can provide consulting 

services to meet your engineering needs. 

Fluent 

3005 Boardwalk, Suite 100 

Ann Arbor, MI 48108 

734 213 6821 

US CFD Summit May 22 – 24 

US Automotive CFD Summit Sept. 11 – 12 

Fluent Sweden Sept. 18 – 19 

Fluent Europe Sept. 20 – 22 

Fluent Benelux Oct. 4 – 6 

Fluent China Nov. 6 – 7 

ATES – Korea Nov. 8 – 10 

Fluent France Nov. 9 

Fluent Germany Nov. 14 

Fluent Asia Pacific Nov. 16 – 17 

Fluent Italy Nov. 21 

Australia Nov. 21 – 22 

Fluent India Nov. 21 – 22 

Fluent in Spain Nov. 24 

International Oil & Gas CFD Conference Nov. 30 – Dec. 1 

Contact your local 

Fluent office for details. 

Fluent Worldwide 

Corporate Headquarters 

Fluent Inc. 

10 Cavendish Court 

Lebanon, NH 03766, USA 

Tel: 603 643 2600 

800 445 4454 

Fax: 603 643 3967 

Email: info@fluent.com 

USA Regional Offices 

Ann Arbor, MI 48108 Austin, TX 78746 

Tel: 734 213 6821 Tel: 512 306 9299 

Santa Clara, CA 95051 Evanston, IL 60201 

Tel: 408 522 8734 Tel: 847 491 0200 

Morgantown, WV 26505 

Tel: 304 598 3770 

European Regional Offices 

Fluent Benelux 

Wavre, Belgium 

Tel: 32 1045 2861 

Email: info@fluent.be 

Fluent Deutschland GmbH 

Darmstadt, Germany 

Tel: 49 6151 36440 

Email: info@fluent.de 

Fluent Europe Ltd. 

Sheffield, England 

Tel: 44 114 281 8888 

Email: info@fluent.co.uk 

Fluent France SA 

Montigny le Bretonneux, France 

Tel: 33 1 3060 9897 

Email: info@fluent.fr 

Fluent Italia 

Milano, Italy 

Tel: 39 02 8901 3378 

Email: info@fluent.it 

Fluent Sweden AB 

Goteborg, Sweden 

Tel: 46 31 771 8780 

Email: info@fluent.se 

Asian Regional Offices 

Fluent Asia Pacific Co., Ltd. 

Tokyo, Japan 

Tel: 81 3 5324 7301 

Email: info@fluent.co.jp 

Osaka, Japan 

Tel: 81 6 6359 7371 

Fluent Software (Shanghai) Co., Ltd. 

Shanghai, China 

Tel: 86 21 53855180 

Email: info_china@fluent.com 

Fluent India Pvt. Ltd. 

Pune, India 

Tel: 91 20 2293770 

Email: info@fluent.co.in 

Distributors 

ANOVA Ltd. – Turkey 

ATES – Korea 

Beijing Hi-Key Technology Corp. Ltd. – China 

Beijing Tianyuan – China (Icepack products only) 

Cavendish Instruments de Mexico, S.A. de 

C.V.(CIM) – Mexico, Venezuela, Argentina, 

Chile, Colombia 

CFD.HU Kft – Hungary 

Flowmen Technology Co., Ltd. – Taiwan 

Fluid Codes Ltd. – UK (sole distributor for the Middle 

East except for Hungary) 

Fluvius Pty. Ltd. – Australia & New Zealand 

J-ROM Ltd. – Israel 

Plasma Venture Ltd. – Russia (serving the Auto, Aero 

and Oil/Gas industries only) 

Process Flow Ltd. – Finland & Baltics; Russia 

(serving Chemical, Power and Electronics industries only) 

Qfinsoft – South Africa 

Simcon Intl (Pvt.) Ltd. – Pakistan 

SimTec Ltd. – Greece (serving Southeastern Europe) 

SMARTtech Fluidos Services & Systems, Ltd. – 

Brazil 

SymKom – Poland 

Techsoft Engineering s.r.o. – Czech Republic & 

Slovak Republic 

TENSOR SRL – Romania

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