Wind Turbine

Automotive 

For the Driver Who Has Everything 

HVAC 

Improving the Air for Arias 

Power Generation 

The Power of SOFC Fuel Cells 

Sports 

The Winning Edge 

VOL XI ISSUE I • SPRING 2002 

APPLIED COMPUTATIONAL FLUID DYNAMICS 

In the 

Wake 

of a 

Wind 

Turbine 

Materials Processing 

Supplement Inside!

Contents 

16 

S2 

feature stories 

30 

5 

wind energy 

In the Wake of a Wind Turbine 

Wind Turbine Blade Aerodynamics 

Mapping a Wind Farm 

36 computing 

FLUENT Users Capitalize on Parallel Processing 

Linux Clusters: Inexpensive Power for High-End 

CFD Computations 

The Impact of the Web on the Engineering 

Simulation Process 

20 

applications 

9 environmental 

UK Water Seminar on Tap 

21 

electronics cooling 

Thermal Modeling of a Multi-Unit Charger 

for Li-ion Batteries 

10 chemical 

Improving Sparger Performance 

Static Mixers by Design 

13 aerospace 

Fatal Concorde Fire Explained 

Unsteady Flow Behind a High 

Speed Train 

16 sports 

The Winning Edge 

19 appliances 

Frost-Free Chilling 

Thermal Mapping of a Hermetic 

Compressor 

23 hvac 

Smoke Management at Frankfurt Airport 

Improving the Air for Arias 

28 automotive 

Customized Phosphate Dip Tanks for Cars 

Arrows Formula 1 Team Moving Up the Grid 

For the Driver Who Has Everything 

32 

power generation 

The Power of SOFC Fuel Cells 

Flameless Burner Validation

materials 

processing 

supplement 

S2 

business case 

Meeting the Wide-Ranging CFD 

Needs of Materials Processing 

S3 

S3 glass 

Reverse-Engineering a Gob 

of Glass 

Ensuring Successful Delivery of 

Molten Glass with CFD 

S4 plastics 

Design Calculator Takes the 

Guesswork Out of Headlight 

Engineering 

Preventing Punctures in Sterile 

Packaging 

S6 semiconductor 

Optimizing Photo-Resist Film 

Uniformity 

Sharp Labs Uses FIDAP to 

Accelerate Promising Flat Panel 

Display Research 

Optimization of Vapor Purging 

in Wafer Isolation Pods 

S8 metallurgy 

Steel Industry Applications at 

ARCELOR 

38 

42 

departments 

34 

product news 

New Specialty Modules for FLUENT 6.0 

Fluent’s Ted Blacker Wins the Meshing Maestro Prize 

36 partnerships 

Cooperative Research on Fuel Cells 

Parameterized Model Building for Climate Control 

Aerosol/Hydrosol Modeling in FLUENT 

Flowmaster Group Announces FLUENTLink 

Turn-key Parallel Computing Solutions 

14 

40 

44 

44 

support corner 

Getting Started with Parallel Processing 

academic news 

Italian University Researcher Wins Prestigious Award 

around fluent 

Fluent Attends Launch of Ferrari Formula 1 Race Car

Editor’s Note 

1986 1993 1997 

On the Cover: 

Line contours of velocity 

magnitude behind a 

wind turbine 

FluentNews is published by 

10 Cavendish Court 

Lebanon, NH 03766 USA 

1-800-445-4454 

© 2002 Fluent Inc. 

All rights reserved. 

FLUENT, FIDAP, GAMBIT, POLYFLOW, 

G/Turbo, MixSim, FlowLab, Icepak, 

and Airpak are trademarks of Fluent 

Incorporated. All other products or 

name brands are trademarks of their 

respective holders. 

Along with 

the steady 

growth of 

our business during 

the past several 

years has been the 

steady growth of our 

corporate newsletter. Launched in April, 1986, 

Volume 1, Number 1 of the Fluent User’s Newsletter 

provided an update on the development of new 

physical models in FLUENT 2.9 (transient flow, 

pressure boundary conditions, and conjugate heat 

transfer, to name a few). It reported on the first 

annual Users’ Group Meeting, and highlighted 

the capabilities of a new product undergoing testing, 

FLUENT/BFC, our first to offer body-fitted 

coordinates. A Frequently Asked Questions section 

focused on issues such as convergence and 

setting turbulence boundary conditions. A twopage 

article on the solution of natural convection 

problems using FLUENT was also featured. 

Since then, the newsletter has tracked the steady 

evolution of simulations performed with our software: 

from simple 2D case studies to complex, 

industrially relevant analyses providing return on 

investment for our customers. The name Fluent 

Inc. Newsletter was introduced in 1993, and with 

it, a full color format. Articles typically dealt with 

modeling advances, validations performed inhouse, 

product updates for solver and pre-processing 

software, and application stories by our 

clients. The title Fluent News was adopted in the 

Spring 1997 issue, along with a new format that 

highlighted a CFD image on the front cover. During 

the next several years, Fluent News underwent 

occasional upgrades as the number and depth 

of the application stories steadily increased. 

With the current issue, we have once again 

undergone a design change to better accommodate 

the increased number and quality of application 

stories submitted by you, our customers. 

Seasoned readers of Fluent News will notice that 

several stories have expanded to two or three 

pages to allow room for more technical details; 

stories about the Frankfurt Airport and the UK 

Sports Institute are examples. Sections with stories 

on related topics have been added; wind energy 

and computing are featured in this issue. 

Application stories continue to abound, with examples 

ranging from air flow inside the Budapest 

Opera House to automotive paint spraying systems. 

The supplement focuses on the breadth 

of applications found in the materials processing 

industry, with contributions from glass, semiconductor, 

steel, and plastics manufacturers. 

The changes we have implemented in this 

issue of Fluent News are the result of our gradually 

changing focus over the past sixteen years 

– from a newsletter in which we tell you about 

how our software works, to a magazine in which 

our customers tell each other about how our software 

works for them. We hope that you can benefit 

from the information contained in the pages 

that follow, and that you will let us know about 

your own experiences with our software. Please 

contact us at fluentnews@fluent.com with 

your comments, suggestions, and stories of your 

successes. ■ 

Best regards, 

Liz Marshall, Editor

wind energy 

In the Wake of a 

Wind Turbine 

by Thomas Hahm and Jürgen Kröning, TÜV Nord e.V., Hamburg, Germany 

Many companies throughout 

the world have been 

applying their skills and 

expertise to the development of 

renewable energy sources. The 

number of companies involved in the 

production of clean and sustainable 

energy will undoubtedly increase in 

the near future due in part to a commitment 

to the Kyoto Protocol 

(1997), which calls for sweeping reductions 

in man-made green-house gas 

emissions, and in part to an increased 

awareness of the environment. 

One of the most abundant 

sources of renewable energy is 

wind, and technology exists today 

for the efficient extraction of energy 

from wind for power generation. 

The efficiency of wind power is tied 

to a number of factors, one of which 

is the positioning of wind turbines 

near other wind turbines or structures. 

Decreased distances give rise to wake 

effects for the downstream units, which 

can lead to changeable wind loads, 

reduced energy yield, and vibration 

induced fatigue on the rotors and 

potentially on nearby power lines. 

One popular operation concept 

for wind turbines allows for adjustments 

in the blade pitch to deliver 

a reasonably constant power output 

when there are variations in the wind 

speed. The wake behind these socalled 

“pitch-regulated” wind turbines 

depends on a number of parameters, 

such as blade geometry, pitch 

angle, and rotor speed on the hardware 

side and wind velocity, turbulence 

characteristics, and wind 

gradients on the environmental 

side. The large number of governing 

parameters makes it difficult to 

judge whether wake influences will 

lead to loads not considered during 

the original construction process. 

In a recent series of simulations at 

TÜV Nord e.V., FLUENT has been used 

to examine the wakes behind wind 

turbines of this type on the basis 

of their geometry and operating 

characteristics. 

TÜV Nord e.V. is one of Germany’s 

Technical Inspection Agencies and 

has the goal of protecting humanity, 

the environment, and property 

against detrimental effects caused by 

technical installations and systems of 

every kind. To this end, it promotes 

the economic installation or manufacture 

and use of technical equipment, 

production, and operating 

facilities. 

In a typical simulation, approximately 

650 data points are used to 

create the geometry of a single rotor 

blade. A fine grid on the whole rotor 

surface is used to create a volume 

Velocity contours behind one turbine show the 

wake effect on a second, smaller turbine 

Fluent NEWS spring 2002 5

wind energy 

The geometry (front) and 

typical surface mesh (back) 

of a turbine rotor and hub 

Velocity magnitude slightly 

downstream of the rotor plane 

mesh of about 750,000 cells that gradually 

coarsens as the distance from 

the blades increases. The dimensions 

of the flow domain are adjusted to 

suit the needs of the specific problem. 

Downstream distances of six to 

ten times the rotor diameter have been 

modeled so far. The multiple reference 

frames (MRF) model is used to 

account for the rotation of the blades. 

Blade pitch, wind speed and direction, 

turbulence intensity and length 

scale, and rotor speed are input for 

each simulation. 

To validate the CFD model, 

wake measurements behind a 55 kW 

pitch-regulated turbine were taken 

from the literature [Ref. 1]. Despite 

some inconsistencies in the measured 

wind velocities, good agreement 

between the measurements and calculated 

values was obtained. In addition, 

calculations presented in 

Reference 1, based on a simpler model 

that did not use the blade geometry, 

were not able to predict flow details 

that were captured by the 3D 

FLUENT runs. In particular, the 

enhancement of wind velocity at the 

edges of the wake could only be predicted 

by the CFD calculations, even 

though the magnitude of the 

enhancement was larger than the 

measured value. 

Once the model was validated, 

it was used for several investigations 

of wake effects. On the previous page, 

one wind turbine is shown operating 

in the wake of a second, larger 

turbine. A wind velocity of 12.5 m/sec, 

with a turbulence intensity of 13%, 

was imposed upstream of the front 

turbine. Filled contours of constant 

mean velocity in the plane of the smaller 

turbine, four diameters behind the 

front turbine, show that the velocity 

field is nonuniform and not centered 

on the hub. Line contours in 

the plane containing the two turbines 

illustrate the decay in the wake as a 

function of distance behind the turbine. 

These results were used to help 

analyze the special wake loads 

experienced by the rear turbine. 

In another example, the excitation 

of vibrations in a power line was 

studied. Wind speeds in the range 

of 1 to 7 m/s and normal to the direction 

of the power line are most likely 

to cause these vibrations [Ref. 2]. 

If there is a considerable shift in the 

wind speeds due to wake loadings 

on the power line, the installation of 

Velocity magnitude in the wake of a wind turbine 

6 Fluent NEWS spring 2002

vibration dampers on the power lines 

might be indicated. In the case studied, 

where the power line runs 25m 

above the ground, well below the 

turbine hub, the wake passes over 

the power line without causing any 

interference. 

Currently, there is little data 

available for the turbulence intensity 

in the vicinity of installed wind turbines, 

and this point requires further 

investigation. Today, different empirical 

models are used to predict turbulence 

intensity in the wake of wind 

turbines [Ref. 3, 4]. Since these models 

only predict single averaged values 

along the wake axis and differ 

from one another, they cannot be 

used to validate the CFD calculations. 

The distribution of turbulence intensity 

computed by FLUENT in the wake 

region is in reasonably good agreement 

with theory. Absolute values, 

however, fall well below measured 

turbulence intensities due to effects 

not captured in the current model 

(e.g. tip vortices and wake meandering). 

Nonetheless, the flexibility 

and increased rigor of the CFD calculations, 

when compared to the simpler 

models, suggests that this 

methodology can offer improved 

insight into the efficient production 

of wind energy in the years to come. 

In summary, given the rotor geometry 

and operating characteristics, CFD 

calculations are able to predict the 

wind velocities inside the wake of a 

wind turbine. Specific operating conditions, 

such as pitch angle and rotor 

speed, can easily be analyzed. Threedimensional 

simulations of wind turbines 

can also be extended to include 

landscape topography (see page 8) 

and other objects located in or near 

the wake. ■ 

Wind Turbine Blade 

Aerodynamics 

by Frank Kelecy, Turbomachinery Application Specialist, Fluent Inc. 

wind energy 

Arecent project funded by the Department of Energy (DOE) 

and the National Renewable Energy Laboratory (NREL) involved 

the study of unsteady blade aerodynamics for large, threebladed 

wind turbines at the National Wind Technology Center (NWTC) 

in Colorado. The project was one component of a larger effort, funded 

by the International Energy Agency (IEA) R&D Wind Executive 

Committee, where field data was collected and analyzed for wind 

turbines operated by five organizations in four different countries. 

Because the incoming wind velocities were not, in general, normal 

to the plane of the rotors, the data collected from all of the 

sites is considered far more insightful than that taken from wind 

tunnel tests. 

At NWTC, a three-bladed, 10m diameter, 20kW Grumman wind 

turbine, operating at a constant speed of 72 rpm, was outfitted 

with 155 surface pressure taps on one of the rotor blades. The taps 

were used to collect data for incoming wind speed and angle, and 

for calculations of turbine power production, and aerodynamic and 

structural modes of the rotor. 

At Fluent, a simulation has been carried out for one of the NWTC 

cases, characterized by an inflow wind speed of 7 m/s, using the 

steady-state, moving reference frame (MRF) model in FLUENT 6. 

The geometry of the wind turbine was simplified for the calculation, 

and consisted of the main blade geometry specified for the 

NREL turbine (an S809 airfoil) along with an idealized cylindrical 

nacelle and spinner. The simpler nacelle geometry allowed a single 

blade to be analyzed due to the circumferential periodicity of 

the flow. An unstructured mesh was used, consisting of 478,664 

tetrahedral cells. The computed pressure distribution on the blades 

was used to determine the shaft power, from which the generator 

power could be derived using available powertrain efficiency 

data. The computed generator power and operating efficiency was 

found to be within 1% of test data from the reported power curve. 

Additional simulations will be performed in order to validate the 

present model over a range of wind speeds. These calculations will 

serve as a benchmark for others who may wish to pursue wind turbine 

modeling projects with FLUENT 6. 

Path lines through the turbine 

colored by velocity magnitude 

References 

1. Beyer, H.G. et. al.; Messungen von Windgeschwindigkeit und Turbulenz in der 

Nachlaufströmung eines 55 kW Windenergiekonverters mit variabler Drehzahl 

(Measurement of windspeed profiles and turbulence in the flow after a 55 kW wind energy 

converter with variable speed); DEWEK ’92, Deutsche Windenergie-Konferenz 1992; 

Wilhelmshaven 1993. 

2. Degener, T.; Kießling, F.; Tzschoppe, J.; Mindestabstand zwischen Windenergieanlagen 

und Freileitungen (Minimum distance between wind energy plants and overhead lines); 

Elektrizitätswirtschaft Jg. 98 (1999), No. 7, p. 32-35. 

3. Dekker, J.W.M.; Pierik, J.T.G. (Eds); European Wind Turbine Standards II; Petten, The 

Netherlands: ECN Solar & Wind Energy, 1998. 

4. Frandsen, St.; Thogersen, L.; Integrated Fatigue Loading for Wind Turbines in Wind 

Farms by Combining Ambient Turbulence and Wakes; Wind Engineering, Vol. 23, 

No. 6, 1999. 

Pressure contours 

on the surface of the 

Grumman 20 kW 

wind turbine 


wind energy 

Mapping a 

Wind Farm 

by Joseph K.W. Lam, Fluent Europe 

A typical wind map, and 

close-up showing the 

locations of the turbines 

Awind farm is a plot of land where a 

number of wind turbines operate concurrently. 

The power delivered by the 

wind to a turbine is proportional to the swept 

area of the rotor blades and the wind speed 

cubed. Wind turbines start to generate electricity 

at wind speeds of about 10 mph, and 

reach their maximum or rated power output 

at about 33 mph. Depending on the 

location, a wind farm will produce electricity 

for about 80-85% of the time, mostly at low 

wind speeds. The site of the farm, in particular 

the topology of the land at and surrounding 

the farm, can play a significant role 

in the efficiency of the collective energy output 

of the turbines. 

At Renewable Energy Systems in the UK, 

FLUENT has been used to predict the wind 

speeds for an existing wind farm at Coal 

Clough, Lancashire. There are 24 turbines 

at Coal Clough providing about 6,000 homes 

with their electricity needs. The analysis was 

done to generate a “wind map”, or high 

resolution contour map of wind speeds at 

a certain height above the ground. The best 

wind maps take into account the variations 

in the local terrain, including the topography 

of the land and the presence of nearby 

structures. A substantial amount of 

measured wind speed data was available, 

and was used for calibration of the CFD results. 

A well calibrated wind map can provide wind 

speeds at every location of the wind farm 

site. Accurate maps for the surface that slices 

through the turbine hub centers are essential 

for planning purposes, especially because 

of the strong dependence of wind speed 

on power. 

For the analysis, a rectangular footprint 

of land was considered that is oriented in 

the direction of the prevailing wind, with 

sufficient upstream and downstream distance 

from the existing core turbine region. Over 

160,000 points of terrain height data were 

used for a 20km wide strip of land, with a 

resolution of 50m horizontally and 1m vertically. 

A mesh of one million hexahedral cells 

was generated. The grid was progressively 

coarsened in the vertical direction, with the 

first cell layer approximately 0.05m off the 

ground and gradually increasing to 25m in 

height at the top boundary of the domain. 

The prevailing wind was found to have 

a height-dependent profile taken from 

anemometer measurements at the site of 

the turbines. The measured velocity profiles 

were applied at the upstream inlet to the 

domain through the use of a user-defined 

function. Because the terrain is hilly near the 

site of the turbines, the resulting CFD predictions 

for velocity at the turbine site were 

greater than the measured values by about 

50% in the initial runs. By calibrating the 

inlet profiles using the measured velocities 

at the turbine, the adjusted predictions at 

the turbines were brought to within 10% 

of the measured values. By repeating this 

process, using anemometer data from 

other nearby turbines and re-calibrating the 

inlet profiles, the wind speed map was developed 

into an accurate tool for predicting the 

flow field at all locations at the site. This project 

will allow the company to explore further 

the potential of CFD, to improve 

knowledge of wind conditions at existing 

and prospective sites. ■ 

© Crown Copyright 

Land topography used as a 

boundary for the simulations 


environmental 

UK Water 

Seminar on Tap 

by Robert Harwood, Fluent Europe 

In December, Fluent Europe Ltd. held a seminar 

on CFD in the Water Industry. With the generous assistance 

of Anglian Water, the seminar took place at Grafham 

Water Treatment Works. 

Grafham Water is one of the largest man-made lakes 

in Europe. It contains nearly 13,000 million gallons (59 

million cubic meters), has a perimeter of 10 miles (16km), 

and at its deepest is 70 feet (21 meters). The site has 

been landscaped and considerable effort has been made 

to ensure that the public is able to enjoy the beauty and 

leisure opportunities of Grafham Water, while Anglian 

Water goes about the business of treating and delivering 

water to its customers. Grafham Water Treatment 

Works can deliver up to 360 million liters of water a day 

with an average daily supply of 230 million liters. 

The seminar opened with an introduction and welcome 

to Grafham and a general presentation on Fluent 

and CFD before the day’s proceedings got underway. 

Dr. Jim Wicks described some of the major CFD projects 

that had been undertaken by Anglian Water, and 

these included: 

• validation of CFD predictions against 

laboratory data for a service reservoir, 

• how £60,000 had been saved in pipework 

costs by using CFD in a service reservoir 

optimization study, and 

• how a 25% improvement in final 

water quality had been achieved by 

recommending a change in dosing 

location. 

Dr. Mike Faram then talked about how FLUENT was 

used at Hydro – a leading supplier of novel and innovative 

separation and flow control devices to the worldwide 

water industry. Their range of products includes 

the Hydrobrake ® Flow Control, Stormcell ® storage media, 

screening systems such as the Hydro-Jet screen, and a 

range of hydrodynamic separators such as the Gritking ® 

and Stormking ® . 

A novel design of combined sewer overflow (CSO) 

chamber, the StormFox, was introduced by Russ Currie 

of Johnston Pipes Ltd. The role of Fluent CFD software 

in fast tracking the design process was discussed. 

Following a demo of FLUENT 6.0, the delegates were 

taken on a tour of the works, providing a suitable end 

to a very enjoyable day. • 

Some of the delegates at the seminar after the tour 

around Grafham Water Treatment Works, with the 

anthracite, sand and garnet (ASG) filters in the 

background. 

Grafham Water (courtesy of Anglian 

Water) 


chemical 

The Lightnin A320 and other internals 

near the base of the vessel 

Improving 

Sparger 

PERFORMANCE 

by Dr. Sang Phil Han, LG Chemicals Ltd., Daejeon, Korea 

The dispersion of gases in liquids is a process that 

is used in the chemical, petrochemical, and pharmaceutical 

industries for fermentation and oxidation 

reactions, synthesis, and the manufacture of fine 

chemicals, for example. Stirred tanks, equipped with a 

gas delivering sparger near the base, are typically used 

for this purpose. If the gas flow rate is high, the behavior 

of the gas-liquid mixture differs considerably from 

that of the liquid alone. The power requirements are 

different as well. While the power required to drive a 

single or multiple impeller system is lowered in the presence 

of the gas, there is an additional power demand 

to operate the sparger. For optimal gas-liquid mixing, 

this device should deliver a uniform flow of gas through 

each of the many holes that cover its surface. 

One of the sparger systems used at LG Chemicals is 

a continuous stirred tank reactor, driven by two Lightnin 

agitators: an A310 near the top of the shaft and an A320 

near the base. The reactor has four baffles, a ring-type 

gas sparger positioned below the A320 with numerous 

side and bottom holes, side circulation inlets, and an 

outlet at the bottom with a vortex breaker and a degassing 

ring. Gas phase reactants are supplied through the sparger 

holes, and liquid phase products are extracted through 

the outlet. A portion of the product stream is recycled 

to the reactor through the side inlet. 


chemical 

The sparger assembly 

For a recent project, several simulations of the reactor 

were performed in an attempt to reduce the pressure 

difference through the sparger holes that had caused 

an overload problem on some of the compressors. In 

order to accomplish the goal without any loss in productivity, 

a decision was made to enlarge the sparger 

hole sizes. Changing the sparger hole sizes had to be 

carefully studied, however, because new problems might 

be introduced in the process. Using FLUENT, several aspects 

of the planned changes that would be critical to successfully 

achieving the goal were checked. First, the flow 

in the sparger itself was precisely investigated for various 

hole sizes. The results were used to assess the distribution 

of the gas flow rate per hole, and to test whether 

the pressure difference for the gas exiting through the 

holes was properly adjusted. Next, the liquid flow pattern 

in the reactor was calculated. These results were 

used to check for possible problems in the mixing patterns 

in the vessel. As a result of this effort, it was found 

that by modifying the agitator system, a better mixing 

pattern could be achieved. The revised liquid solution 

was then used as the basis for the gas sparging calculation, 

which was performed using the discrete phase 

model (DPM). This calculation was used to ensure that 

the hole size proposed in the first phase of the project 

would not lead to any unforeseen problems when the 

sparger was activated. During this phase of the project, 

the underlying assumptions for the DPM were validated, 

and the fundamental concepts for bubble formation 

by a gas emitted from a sparger hole in a liquid were 

investigated. 

As a result of the project work, the most appropriate 

hole sizes for the spargers was chosen that would 

satisfy the process goals while introducing no unexpected 

problems in reactor operation. The results also helped 

identify ways to modify other aspects of the agitating 

system so that better gas dispersion could be obtained. 

All of the ideas have since been applied in the field, and 

the reactor is now operating successfully. ■ 

Path lines illustrate some of the bubble trajectories 

The gas flow 

in the sparger 


chemical 

SMX mixer geometry 

Static Mixers 

by Design 

by Shiping Liu, Andrew Hrymak, and Phil Wood, 

McMaster University, Hamilton, Ontario, Canada; 

and Rafiqul Khan, Fluent Inc. 

Static mixers consist of an array of 

similar, stationary mixing elements, 

placed one behind the other in a pipe 

or channel. Liquids are pumped through 

the channel, and the elements act to accelerate 

the homogenization of material properties, 

such as concentration, temperature, 

and velocity. In some types of static mixers, 

the elements are rotated by some angle 

(say, 90°) relative to the previous element. 

The SMX mixer is one example of this type 

of mixer. The elements are complex networks 

of angled guide blades, positioned 

at an angle to the pipe axis, and mixing 

occurs through the continuous redirecting, 

splitting, stretching, and diffusion of the 

fluids as they pass through the available 

openings. 

Since there are no moving parts 

involved, static mixing occurs with low shear, 

which is very important for some mixing 

processes where gentle treatment of the 

materials is required. Processes of this type 

are found in the food processing, pharmaceutical, 

and biotechnology industries. 

Static mixers are also widely used in a host 

of other industries, however, including oil 

and gas, chemical processing, polymer production 

and processing, and water and waste 

treatment. Some of the major manufacturers 

of static mixers are Sulzer Ltd., Koch-Glitsch 

Inc., and Chemineer Inc. 

Researchers from the Department of 

Chemical Engineering at McMaster University 

have been investigating the laminar mixing 

characteristics of an SMX static mixer 

using the discrete phase model (DPM) in 

FLUENT. Typically a series of SMX elements 

is used to ensure adequate mixing. The mixing 

quality increases with the number of 

mixing elements, but so does the power 

required to pump the fluids through the 

channel. For this reason, the number of mixing 

elements used in any given mixer is a 

function of the required product quality and 

operating budget. 

Mixing homogeneity is often rated using 

the coefficient of variation, or COV, which 

can be approximated using the fluid properties, 

operating parameters, and geometry 

of the mixing element. It can also be 

computed easily using CFD. Furthermore, 

CFD can be used to test the COV after the 

fluid has passed through different element 

designs, and to determine the minimum 

number of elements required to achieve the 

desired product quality. With CFD, these 

parameters can be established long before 

construction of an experimental apparatus 

begins, saving both time and money. 

Using FLUENT, COV values, pressure drop, 

and power requirements have been computed 

for a series of test cases using four 

SMX elements in a pipe. Qualitative 

results from the DPM calculations have clearly 

shown the expected stretching and layering 

of the fluid during the mixing process. 

Simulations using a two species model to 

track the mixing of epoxy resins have also 

been performed, and the results, particularly 

the species distribution on several axial 

planes, are in close agreement with experimental 

data provided by Sulzer for the SMX 

mixer. ■ 

Using the DPM, the particle distribution through the mixer, 

using a central feeding of 20,000 tracers is shown 

Using the species mixing approach, concentration contours 

on the center plane are shown 


aerospace 

Afatal accident in July 2000 involving an Air France 

Concorde near the Charles De Gaulle Airport in 

Paris led to the temporary grounding of the entire 

fleet of these supersonic passenger planes. An investigation 

into the crash revealed that a metal strip had fallen 

off an aircraft previously departing from the 

runway. When the Concorde taxied over the shard, its 

tires burst, sending several pieces of rubber flying into 

the air. One piece struck the left wing fuel tank of the 

airplane, rupturing it. The leaking aviation fuel ignited 

near the left engine, causing a huge flame to erupt behind 

the aircraft. The altered aerodynamics made it impossible 

for the seasoned pilot to control the plane as it lifted 

off from the runway. Tragically, the Concorde crashed 

near the airport, killing all people on board and some 

on the ground. 

As part of the investigation to explain the accident, 

researchers at the University of Leeds were encouraged 

by John Tilston, QinetiQ, who worked on behalf of the 

Air Accident Investigation Board (AAIB), to look into the 

reason why the fire stabilized on the wing once it started. 

They used the VOF model in FLUENT to understand 

the flow characteristics of the leaking fuel that gave rise 

to the observed flame formation. A CFD model of the 

delta wing of the Concorde, minus the fuselage, was 

created. (The fuselage was judged to have little or no 

impact on the development of the leaking fuel jet.) Several 

simulations were performed using an estimated takeoff 

speed of 100m/s (224 mph) and a range of attack 

angles that matched amateur photos of the incident. 

In each model a steady stream of fuel was discharged 

into the CFD domain from a small hole on the underside 

of the aircraft wing. Both the k-ε and Spalart-Allmaras 

turbulence models were employed in the study, both 

of which led to similar results. 

The FLUENT predictions indicated that a very complex, 

recirculating flow structure developed under the 

wing as the aircraft lifted off, particularly inside the wheel 

bay. This result suggested that large recirculating air cells 

in the landing gear bay provided a suitably stable attachment 

point for the flame once it was ignited, probably 

by an electrical spark. The predicted fuel trajectory was 

mainly confined to a small area under the wing that closely 

matched the observed flame in the amateur footage 

of the crash. This was a qualitative verification of the 

conclusions drawn by the model. The CFD study, plus 

other recent studies on how to improve fuel tanks for 

the Concorde fleet, has led to modifications that should 

prevent a similar incident from happening in the future. 

The modified Concorde airliners were reintroduced to 

commercial service in October 2001, and the operational 

fleet is now fully functional. ■ 

Fatal 

Concorde 

Fire Explained 

by L. Ma and M. Pourkashanian, Leeds University (CFD Center), Leeds, 

Yorkshire, UK, and J. Tilston, QinetiQ, Hampshire, UK 

GAMBIT Mesh for the delta wing simulation 

Predicted CFD cold fuel plume from ruptured 

left wing fuel tank during take off 


aerospace 

ICE 2 end car 

Unsteady Flow Behind a 

by Dr. Christoph Heine and Gerd Matschke, Deutsche Bahn AG, Munich, Germany 

Oil-flow path lines, colored by pressure, show 

the flow patterns on the end car surface 

Modern trains are lighter 

than those of past years. 

This is due in part to the 

replacement of a power car at the 

rear of the train with an unpowered 

driving trailer. This change has meant 

lower axle loads, reduced wear on 

ballast, and increased passenger 

capacity, since the end car can now 

be filled with seats. 

For a light-bodied driving trailer, 

the unsteady aerodynamic loads may 

become significant for the running 

behavior, and this effect has become 

a concern for a number of railway 

operators in Europe. In the BriteEuramfunded 

research project RAPIDE 

(Railway Aerodynamics of Passing and 

Interaction with Dynamic Effects), the 

partners have joined forces to investigate 

the boundary layer development 

along a modern high-speed train 

and the wake flow characteristics 

behind the end car using CFD. 

The CFD investigation was divided 

into three parts, corresponding 

to three sections of a moving 

train: the front car, the six mid-cars, 

and the trailing car. The boundary 

layer grows in thickness from the front 

to the trailing car, and when this thick 

boundary layer separates behind the 

trailing car, the points of separation 

on the train surface can periodically 

shift. This gives rise to aerodynamic 

oscillations about the longitudinal 

axis, which can cause discomfort 

to the passengers riding in the trailing 

car. The European organizations 

MIRA and SNCF performed boundary 

layer development calculations 

on the front and mid-car sections, 

respectively. Their results were then 

used by Deutsche Bahn to simulate 

the unsteady flow around and 

behind the German ICE 2 end car. 

The end section modeled was 

40m in length and positioned in a 


aerospace 

domain of length 60m, width 

20m, and height 15m. A volumetric 

mesh of tetrahedral and prismatic 

cells was used. The profiles along the 

sides and on top of the train generated 

by the other partners in the 

project were used as inlet boundary 

conditions. The ground under 

the train was given a uniform speed 

equal to that of the moving train. 

A steady-state simulation using 

the k-ε turbulence model was initially 

performed on multiple processors. 

The symmetric solution showed 

low pressure on the shoulder areas 

of the end car and a high pressure 

region on the back face that results 

from the onset of separation. A transient 

calculation was then initiated 

using the steady solution as a starting 

point. Using time steps of up to 

0.01s, unsteady flow developed with 

a period of oscillation on the order 

of 1 Hz. This frequency was found 

to be in good agreement with measurements 

reported by a Japanese railway 

company 1 . Further runs were 

done using smaller time steps and 

a higher order turbulence model 

(RSM), yielding identical oscillations 

in the flow. Based on the CFD results, 

the aerodynamic coefficients were 

calculated. These forces and moments 

served as an input for Multi Body 

Systems (MBS) calculations performed 

by Bombardier Transportation, and 

the running comfort was evaluated. 

Luckily, the oscillations were found 

to be far too weak to cause vehicle 

movements, so they would not cause 

any passenger discomfort. ■ 

references 

1 Kohama, Y., Koshikawa, T. and 

Okude, Wake Characteristics of a High 

Speed Train in Relation to Tail Coach 

Oscillations, Vehicle Aerodynamics 

Conference, Loughbuough Univ., 

UK, 1994. steady unsteady 

Comparison of surface pressure for the steady and unsteady cases 

High Speed Train 

Path lines and planes showing velocity magnitude contours behind the train 


sports 

The 

Dr. Richard Young at the UKSI 

competed in the sport of cycling 

at the 1988 and 1992 Olympics 

while completing a degree in 

biomechanics 

Winning 

Edge 

by Richard Young, Technology and Innovation Coordinator, UKSI, 

London, England 

Today, victory in sport is a matter 

of a fraction of a second or 

a few millimeters separating first 

and second place. Therefore any legal, 

cost-effective, and performanceenhancing 

technology has to be taken 

seriously, especially given the 

amount of money associated with 

winning. Whole new scientific disciplines 

like sports psychology, 

sports nutrition, and sports biomechanics 

have developed over the 

last 30 years, and have become part 

of the supporting framework behind 

elite sportsmen and women around 

the world. During the last five to ten 

years, rather late into the fray, sports 

engineers and technologists have also 

emerged, and their contributions to 

the engineering and technological 

aspects of sports equipment and athlete 

biomechanics have gained 

increasing acceptance. All of these 

disciplines have combined to help 

continually improve elite performance 

in sport. 

It has long been accepted that 

an understanding of fluid flow phenomena 

could lead to performance 

enhancements for certain competitive 

sports, especially those 

dominated by aerodynamics and 

hydrodynamics. Over the years, 

FLUENT has been used for a number 

of pioneering simulations of this 

type, such as motor racing, ski jumping, 

yachting, and sports ball modeling. 

Results have been used to 

optimize the balance between drag 

and downforce (motor racing), to 

illustrate why one posture is better 

than another (ski jumping), to 

perfect the design of a winged keel 

(yachting), and to better understand 

the impact of laces and geometric 

patterns on flight (sports balls). 

Performance enhancements that result 

from analyses like these will undoubtedly 

lead to the continued expansion 

of sports engineering in the years 

to come through the use of CFD. 

In the United Kingdom, the concept 

of a sports institute, dedicated 

to understanding and improving 

performance, was first discussed in 

1995. In October 2000, the idea 

became a reality as the United 

Kingdom Sports Institute (UKSI) 

opened in London. Sports institutes 

of this type are not new; many have 

been established around the world 

during the last ten years. All, and 

especially the Australian Institute of 

Sport, have helped contribute to 

notable sporting successes. These government-funded 

organizations, 

16 Fluent NEWS spring 2002 

Olympic cyclists in team pursuit formation 

Courtesy of the International Sports Engineering Association

sports 

which are primarily aimed at helping 

Olympic athletes, seek to provide 

elite competitors with the facilities 

and leading edge support necessary 

to help them excel at the pinnacle 

of their sport. 

It was with this ideal in mind that 

the UKSI has begun to investigate 

some of the fundamentals of flow 

applications in Olympic sports 

using FLUENT, with the hope of helping 

elite athletes on the British 

Olympic and Paralympic teams. To 

date, technological advances have 

played a major role in many 

Olympic sports, such as pole vaulting, 

cycling, and skiing, resulting in 

better equipment and refined techniques. 

Many of these advances have 

not been systematically studied, however, 

and some of the underlying 

engineering phenomena have never 

been fully understood. Through the 

use of CFD, many of these knowledge 

gaps can be filled. At the UKSI, 

this technology has been identified 

as having the potential to produce 

significant performance gains for elite 

athletes. Fluent’s software has been 

proven to be successful in other competitive 

sports and is head and shoulders 

better than other CFD codes 

for sports applications. 

crosswind effects 

on cyclists 

Cycling is one Olympic sport 

where CFD can help illuminate several 

flow phenomena. Applications 

for CFD in this sport are many, including 

cycle aerodynamic design, 

cyclist posture, helmet design, and 

optimal cyclist drafting positions during 

pursuit races. One area where 

cyclists do not agree, however, is on 

the selection of rear wheel type in 

a crosswind. While disk wheels 

become unmanageable for the 

front of a bicycle on windy days, the 

choice between disk and the traditional 

spoked wheels for the rear 

continues to undergo vigorous 

debate. 

It has been speculated that the 

rear disk wheel could act as a sail 

in certain circumstances, providing 

a forward force in the rolling direction 

opposite the drag force, and 

hence reducing the net drag experienced 

by the cyclist. Although many 

cyclists use rear disk wheels to try 

to capitalize on this lift, there has 

been little clear evidence to support 

its existence. An analysis of wheel 

performance would add to the growing 

body of knowledge that CFD has 

provided to date for cycling applications, 

much of which cannot be 

easily obtained from wind tunnel tests. 

In the CFD study carried out, simulations 

using FLUENT were applied 

to a generic geometrical representation 

of a cyclist and bike created 

in GAMBIT. All crosswinds were simulated 

as constant and steady at 90° 

to the direction of motion of the 

cyclist. Calculations were performed 

for a cyclist using a spoked front wheel 

at a forward speed of 25 mph, in 

crosswind speeds varying from still 

air to 30 mph, with spoked and disk 

rear wheels. Since the same CFD 

mesh was used for each simulation, 

it was felt that it should lead to the 

predicted trends being accurately 

resolved. 

In crosswinds, the cyclist experiences 

a drag force (opposing the 

direction of motion) and a side force. 

While the cyclist only has to work 

against the drag force, the CFD calculations 

showed an increase in the 

magnitude of the drag force for both 

types of rear wheels when a crosswind 

is present. The net drag force 

predicted by FLUENT as a function 

of wind speed shows that in still air, 

the advantage of using a rear disk 

wheel over a spoked wheel is negligible 

(about 2%). As the wind speed 


sports 

FlowLab 1.0 is 

Released! 

Virtual Fluids Laboratory 

for Engineering Education 

Flow path lines around a cyclist with a spoked 

rear wheel in a 20 mph crosswind (top) and a 

disk rear wheel (bottom) 

Bring the power of CFD 

to the classroom: 

•Reinforce fundamental 

concepts 

•Expand lab experiences – 

easily and economically 

•Stimulate interest in fluid 

mechanics 

•Expose students to essential 

job skills 

•Use pre-defined examples 

or customize your own 

increases, however, the advantage of 

the disk wheel improves dramatically 

owing to the “sail effect.” In a 20 mph 

cross wind, the net drag experienced 

by the cyclist is 17% lower with the disk 

wheel than with the spoked wheel, suggesting 

that the disk wheel gives an apparently 

overwhelming advantage. 

There are practical disadvantages to 

disk wheels though. For example, a disk 

wheel creates significantly larger side 

forces. In a 20 mph crosswind, the side 

force acting on the cyclist plus bicycle 

with a rear disk wheel is approximately 

double that for a cyclist using a spoked 

rear wheel. The trade-off for the cyclist 

is, therefore, one of stability, especially 

in a gusting wind. In reality, the situation 

is complicated further by 

variability of wind and rolling directions, 

and shielding by surrounding objects 

(including, in stage races, the other 

cyclists). The message from the simulations 

is clear, however. The cyclist can 

move moderately to significantly faster 

for the same power output, using the 

rear disk wheel rather than a spoked 

wheel, confirming the empirical observations 

experienced by many top-notch 

cyclists. ■ 

more.info@ 

flowlab.fluent.com 

flowlab@fluent.com 

Graph of relative drag difference between a cyclist using a rear wheel with and 

without a disk in a range of crosswinds 


appliances 

Frost-Free 

Chilling 

by Graham Sands and Weizhong Xiang, General Domestic Appliances, Peterborough, Cambridgeshire, England 

Mesh scheme of the freezer 

General Domestic Appliances (GDA) Ltd. is the largest manufacturer 

of domestic appliances in the UK, with products 

that include refrigerators, stoves, washing machines, clothes 

dryers, dishwashers, and more. GDA began using FLUENT in April 

2001. The first of their projects to make extensive use of CFD was 

the development of a new line of frost-free refrigeration appliances. 

One of the main goals of the project was to design the refrigerators 

with improved energy performance, to cut operating costs. To 

reduce the energy demands of the units, two aspects of the airflow 

inside the refrigerators had to be optimized. First, the maximum air 

flow rate had to be generated using the smallest possible fan. This 

would not only improve the efficiency, but would also make the unit 

run more quietly. Second, the fan(s) and other internals needed to 

be positioned in such a way that the airflow inside both the refrigerator 

and freezer units was distributed in the most efficient way. 

Test rigs were constructed so that measurements could be made in 

parallel with the CFD simulations. The role of these rigs was to validate 

the results of the CFD simulations and carry out the airflow 

optimization phase of the project. 

The largest freezer studied in this project was 1.8 meters high 

and had 9 baskets. Because the geometry of the freezer is very complicated, 

with small gaps between the food packs and baskets, a tetrahedral 

mesh was used. The results for pressure distribution indicated 

that the largest pressure losses were occurring below and behind 

the bottom basket. This result was validated by measurements on 

the test rig. After increasing the clearance between the baskets and 

inside walls, the simulation was repeated, and the total airflow rate 

of the freezer was found to increase considerably. 

The model was also used to study the pack temperature distribution 

in the freezer. A steady-state simulation was performed for a 

case where the compressor was running 100% of the time, and a 

transient simulation was performed when the compressor was cycling 

on and off. The results for the steady-state case (top right) suggested 

that the top and bottom basket have the warmest pack temperature 

if the air is uniformly distributed in the freezer. When the compressor 

runs intermittently, however, the top basket has the warmest 

pack temperature. In order to reduce the pack temperature near the 

top and bottom baskets, the simulations showed that more air should 

be introduced to these regions. 

At GDA, FLUENT has been proven to be a useful tool to assist 

the development of frost-free refrigerators. It has been used successfully 

to identify problems before any prototype models were built. Models 

of other appliances have since been developed and these models 

have provided further useful information for design decision making, 

and have assisted in the product development process. ■ 

Pack temperature distribution 

in the freezer 

Pressure distribution in the freezer 


appliances 

Thermal 

Mapping of 

a Hermetic 

Compressor 

Temperature distribution on the internal 

pump assembly 

by Rahul Chikurde and S. Manivasagam, Kirloskar Copeland Ltd., Karad, India 

The complex fluid flow and heat transfer 

phenomena in hermetic compressors are 

very difficult to analyze theoretically. Because 

there is insufficient understanding of the physics 

involved, assumptions are often made in order 

to solve these problems analytically, and these 

assumptions can have a negative impact on 

the quality of the results. To cope with today’s 

high-energy efficiency standards, there is a need 

to overcome these limitations, so that the flow 

and heat transfer inside the compressor can 

be better understood. 

At Kirloskar Copeland in Karad, India, CFD 

has been used to perform a more rigorous analysis 

of the entire compressor domain, including 

the suction and discharge gas paths. The 

ability of the FLUENT code to deal with conjugate 

heat transfer (conduction and convection) 

in a turbulent flow encouraged engineers to 

perform a flow and thermal analysis for the 

entire compressor. The effort has helped predict 

such important characteristics as motor 

winding temperature, and velocity and pressure 

fields across the domain. The powerful 

visualization tools have made it easy to see the 

overall flow patterns along the gas flow paths. 

The thermal performance of the compressor 

plays an important role in the optimal working 

of the appliance in which it is fitted. Hence, 

it is necessary to carefully simulate the heat 

transfer inside the compressor, since it governs 

the energy efficiency of the whole system. 

The most important contributors to the 

thermal performance are the suction gas superheating, 

which is mainly due to heat sources 


related to the copper and iron (or core) losses 

and the heat of compression, and volumetric 

and energy losses occurring in the suction and 

discharge gas paths. Other heat sources inside 

the compressor are due to rotor and frictional 

losses. Each of these effects is represented by 

a volumetric heat source in the FLUENT model. 

To date, the CFD analysis has provided predictions 

for the temperatures on numerous 

components inside the compressor. This information 

has been used to help design more 

efficient motors (with better cooling) and select 

the appropriate Internal Overload Protector 

(OLP), which protects the motor from overheating 

under adverse conditions. 

The results of the numerical simulation have 

been validated using an experimental set-up 

that uses conventional thermocouples to perform 

thermal mapping of the compressor. The 

numerical solution has been found to agree 

well with the experimental results. Because 

the simulation resembles the actual testing of 

the compressor on the calorimeter test rig under 

specified conditions, the compressor behavior 

can be visualized and thoroughly understood 

well before the prototypes are built and 

tested. If need be, the compressor design can 

be altered to obtain the target performance. 

The success of the validation work has given 

Kirloskar Copeland engineers the necessary confidence 

to use CFD during the product development 

stage for new equipment, thereby 

reducing the number of prototypes for trial 

and error, and the total design cycle time by 

almost 30%. ■ 

Path lines illustrate the flow through 

the compressor 

Temperature distribution on a vertical plane 

through the crankshaft axis


Thermal Modeling 

of a Multi-Unit Charger 

for Li-ion Batteries 

by Hossein Maleki, John Johnson and Kevin Kitts, Motorola Energy System Group (ESG), Lawrenceville, GA 

Demands for small and high power sources 

to operate portable electronics and their 

associated accessories are continuing 

to increase. Among these demands are 

increased power and reduced size for lithiumion 

(Li-ion) battery packs and their associated 

charging units. Li-ion batteries have 

become the power source of choice for portable 

electronics because of their high energy density, 

rate capability, and long cycle-life. 

However, they tend to self-heat during 

charge and discharge cycles, and lose capacity 

if exposed to or operated at temperatures 

greater than 65°C. 

To charge a Li-ion battery, a charger needs 

to apply a controlled current to increase the 

Li-ion cell voltage from about 3.0 V to no more 

than 4.2 V. Overcharging could lead to capacity 

fading and thermal stability issues. Multiunit 

chargers are more economical to operate 

than single-unit chargers, but they can run at 

higher temperatures, causing potential damage 

to the batteries and control electronics. 

Motorola Energy System Group (ESG), a 

leading provider of complete energy system 

solutions for portable electronics, such as cell 

phones and laptop computers, has used Icepak 

to address thermal management issues related 

to a multi-unit charger for Li-ion batteries. 

This effort has allowed engineers to simulate 

the product’s thermal response for a given set 

of customer specifications, and confirm or make 

changes to the design before a new product 

is built. 

Using Icepak, an eight-unit charger with 

maximum natural convection cooling was simulated. 

Early design validations demonstrated 

that Icepak predictions of temperature at 

several sites on the charger were in good agreement 

with measured data (see table at right). 

Through subsequent modeling, it was determined 

early in the design phase that the cus- 

The internal peak temperature rise of the charger when fuel gauging (calibrating) eight batteries 

simultaneously is shown. The temperature of the load resistors (location 2) rises to ~88°C. Modeling also 

showed that the heat that evolves mainly from the load resistors causes the temperature of the back of the 

aluminum (Al) base (location 4) to rise above the critical limit (55°C), set by UL for metallic parts that 

could be touched by the end users. 

Temperature (°C) 

8-Batteries Discharge 

Location /Part Experiment Modeling 

1 Power Supply 54 52-56 

2 Load Resistors 92 88 

3 Logic ICs 56 54 

4 Chassis Back Exterior (AL, 3mm) 58 58-69 

5 Cell Pocket Bottom Interior (PC/ABS) 44 45 

6 Back Housing Over the Vent 47 45-51 

7 Chassis Exterior Bottom (Al) 51 55 

8 Chassis Exterior (Al) Under Load Resistors 80 78-81 

The table above compares Icepak predictions to experimental data obtained while 

the unit calibrated eight batteries simultaneously 



Fin cooling (top) and fan cooling (bottom) show the temperature distribution on 

the outside surface of the charger. In both cases, the simulation was conducted 

with four batteries being charged and four batteries being discharged. Both 

configurations caused the charger to exceed the allowed upper temperature limit 

(55°C). 

tomer’s time-frame requirement for charging 

or calibrating (discharging a fully charged cell 

for capacity check) all eight batteries simultaneously 

was not possible. The charge step caused 

the temperature of the power supply to rise 

above its optimum operating temperature. 

Calibrating affected heat dissipation from the 

Li-ion cells and their associated load resistors. 

Icepak was also used to evaluate the effects 

of fan cooling versus fin cooling on the operating 

temperature of the unit while simultaneously 

discharging four batteries and 

charging four batteries. Results showed that 

the addition of a fan, meeting cost and design 

limitations, provides 15-17% more cooling to 

some parts of the charger. 

After a number of modifications were tested, 

a final design was chosen. The series of 

simulations showed that the eight-unit charger, 

meeting customer design requirements, 

is capable of calibrating only three batteries, 

while charging five at the same time. This optimized 

solution, which includes detailed 

operating temperature information for all charger 

components, could not have been obtained 

without the combined strengths of the ESG 

engineering staff and Icepak software. The simulations 

demonstrated not only the limitations 

of the existing design, but also alternative solutions 

to improve the thermal performance of 

a multi-unit charger. At Motorola ESG, CFD 

modeling with Icepak has proved to be a costeffective 

tool for predicting the thermal response 

of electronic power sources. ■ 

This charger has fins placed on the 

backside of the printed circuit board 

(PCB) beneath the load resistors. 

Additional modifications in this model 

included increasing the height of the 

back-wall of the Al-base, and thermal 

isolation of the back end of the PCB 

from the Al-base. These changes led 

to better cooling of the Al-base, 

maintaining a temperature below 

55°C. 


FOCUS on CFD 

For Materials Processing 

Newsletter Supplement 

S2 

business case 

Meeting the Wide-Ranging CFD 

Needs of Materials Processing 

S3 glass 

Reverse-Engineering a Gob 

of Glass 

Ensuring Successful Delivery of 

Molten Glass with CFD 

materials processing 

S4 plastics 

Design Calculator Takes the 

Guesswork Out of Headlight 

Engineering 

Preventing Punctures in Sterile 

Packaging 

S6 semiconductor 

Optimizing Photo-Resist Film 

Uniformity 


Accelerate Promising Flat 

Panel Display Research 



S8 metallurgy 

Steel Industry Applications at 

ARCELOR 

CFD: 

Showerhead in a 300 mm 

thermal CVD reactor 

Courtesy of Novellus Systems, Inc. 

In background: 

Concept Two Dual ALTUS 

tungsten process chamber 

Courtesy of Novellus Systems, Inc.

usiness case 


Meeting the Wide- 

Ranging CFD Needs of 

Materials 

Processing 

by Eric Grald, Materials Industry Director, Fluent Inc. 

The term “materials processing” conjures up 

an amazingly wide range of applications and 

industries, including (but certainly not limited 

to) semiconductor manufacturing, glass, polymers, 

non-woven materials, consumer products, 

food, and metals. The analysis needs of these 

industries are similarly broad, not to mention complex: 

chemical reactions, plasma physics, multiphase 

flow, radiation, phase change, generalized 

non-Newtonian rheology, free surfaces, fluid-structure 

interaction, porous media, and many more. 

Fluent is able to meet these diverse needs 

through a trio of industry-leading products: FLU- 

ENT, FIDAP, and POLYFLOW. By drawing on the 

unique strengths of these programs, customers 

are able to realize the true potential of CFD by: 

• reducing the time and expense of 

developing new products, 

• troubleshooting existing products 

and processes, 

• decreasing the number of 

prototypes needed, 

• gaining invaluable physical insight 

into their problems. 

These benefits have become reality because 

of the tremendous advances in CFD in recent 

years, many pioneered by Fluent. One of the main 

goals is to improve productivity by reducing the 

time required to create the CFD model and obtain 

the solution. The direct import of CAD models, 

extensive use of unstructured meshes, and automated 

meshing techniques have greatly reduced 

the time required for preprocessing. To further 

reduce the turnaround time, more and more users 

are taking advantage of parallel processing capabilities 

with multi-processor computers and networks 

of workstations. To extend the capabilities 

of the software, many users have taken advantage 

of user-defined subroutines and functions. 

Specialty modules are available to simulate continuous 

fiber manufacturing, magnetohydrodynamics 

(MHD), and glass batch melting, electrical 

boosting, and bubbling (see Product News on 

page 34). 

Another way that leading edge physical models 

are incorporated into Fluent’s products is through 

partnerships with technology leaders. In a partnership 

with Kinema Research and Software, 

FLUENT has been linked with the plasma simulation 

program PLASMATOR ® to address plasma-enhanced 

chemical vapor deposition, 

dielectric and metal etching, ion implantation, 

and reactor cleaning (see Addressing Plasma 

Processing, Fluent News, Fall 2000). The resulting 

3D simulations are fast enough to allow design 

iterations in an industrial time frame. The Fine 

Particle Model, developed by Chimera Technologies, 

allows the simulation of aerosol and hydrosol 

formation, growth/shrinkage, transport and deposition 

(see Partnerships on page 42). The integration 

of CFD with flowsheet models is being 

accomplished by a partnership between Fluent, 

AspenTech, ALSTOM Power, Intergraph, and West 

Virginia University in the Vision21 project funded 

by the U.S. Department of Energy (see Vision 

Above, melt blown die for non-wovens 

manufacturing: instantaneous flow field (velocity 

vectors) reveals large scale eddy structure 

Below left, temperature differential in a crutcher 

used for detergent manufacturing 

21 Update, Fluent News, Fall 2001). By incorporating 

a detailed CFD model (such as a stirred 

tank reactor) into the flowsheet model of the 

entire system, engineers can be certain that fluid 

flow details are accurately accounted for as the 

process is designed and optimized. 

The examples in this supplement provide a 

sample of the different ways customers have applied 

Fluent software to solve their real-world problems. 

We hope it will offer an appreciation for 

the diverse world of applications known as “materials 

processing.” The future holds many more 

challenges in this area, and Fluent is working hard 

to expand the scope and capability of CFD to 

meet these challenges. ■ 

S2 Fluent NEWS spring 2002

glass 

Reverse-Engineering 

a Gob of Glass 

by Matthew R. Hyre, Virginia Military Institute, Lexington, VA 

Gob formation 

at the feeder 

Industrial glass container forming is a complex sequence 

of unit processes that leads up to the actual forming process 

in an individual section machine. The forming process can 

be roughly divided into several steps that begin with the 

formation of a glass gob at the feeder, followed by the transfer 

and loading of the gob into a blank mold. The shape of 

the glass gob and its orientation before it falls are important 

components of the manufacturing process of many glass 

products. Large deviations from the ideal gob shape and 

trajectory can have severe consequences on the penetration 

of the glass into the transfer equipment and molds, and 

asymmetric loading of the gob into the blank molds can 

cause uneven temperature and wear patterns on the mold 

interiors. Traditionally, gob shape control has been conducted 

by trial and error based on past experience and operator 

knowledge, but recent advances in numerical techniques 

and computer capabilities have made the numerical modeling 

of the gob forming processes feasible. 

A numerical study was performed recently using 

POLYFLOW to investigate the importance of the initial gob 

formation and transfer on the formation of glass bottles. The 

simulation modeled the formation of the gob at the feeder, 

and the transfer of the gob to the blank mold. Techniques 

such as thermo-mechanical coupling, mesh-to-mesh interpolation, 

and mesh superposition of the plungers on the 

glass were employed. Remeshing techniques were used that 

allowed a continuation of the calculations despite very severe 

mesh deformations. By evaluating the extent to which feeder 

plunger motion and gob transfer equipment affect gob 

shape and weight, a systematic methodology to control these 

parameters can be developed. ■ 

Ensuring Successful Delivery 

of Molten Glass with CFD 

by Christopher Jian, Owens Corning, Granville, OH 

As the world’s leading glass fiber and materials manufacturer, Owens Corning 

is committed to delivering products of the highest quality to its customers. 

One of the critical processes in the manufacture of continuous strand glass 

fiber is the front-end glass delivery system. The front-end system consists of various 

covered channels and forehearths made of refractory materials. Channels are 

used to deliver glass from the melter to a network of product-forming stations, 

and to provide a means of thermally conditioning the glass to the required temperatures 

by applying cooling or heating along the way. Forehearths are used to 

distribute glass to each forming station while maintaining glass temperatures dictated 

by the forming products. It is crucial that the front-end system delivers glass 

of the highest quality to the forming operations, both chemically and thermally, 

to insure that the products meet customers’ highest quality standards. 

In order to meet the stringent requirements of fiber forming operations, significant 

effort has been devoted to the design, engineering, and operation of these 

front-end systems. Engineers at Owens Corning have successfully integrated CFD 

modeling in the overall process. Coupled with an in-house computer code, 

FLUENT is used for modeling both the combustion space and the glass flow. Extensive 

validation of the CFD model against field measurements has been performed, to 

ensure the accuracy and integrity of the simulation results. The CFD model has 

become an integral tool for improving the design and operation of front-end glass 

delivery systems. It is also being used to make engineering and business decisions 

that have resulted in significant capital and operating savings. Currently, this frontend 

CFD model is being integrated with Owens Corning’s forming technology model 

to maximize the potential of numerical simulation. ■ 

103 

Temperature in a fiberglass front-end 

103 


Gob transfer 

to a blank 

mold 

normalized glass temperature 

102 

101 

100 

99 

98 

97 

FLUENT 

Measurement 

normalized glass temperature 

102 

101 

100 

99 

FLUENT 

Measurement 

96 

0.65 

98 

0.0 

0.75 0.85 0.95 1.05 

normalized glass depth 

0.2 0.4 0.6 0.8 1.0 

normalized glass depth 

Temperature validation in a channel 

Temperature validation in a forehearth 

Fluent NEWS spring 2002 

S3

plastics 


Design Calculator Takes 

the Guesswork Out of 

Headlight 

Engineering 

by Eric Jaarda, GE Plastics, Southfield, MI 

Material selection decisions are becoming 

increasingly critical in automotive 

lighting. The drive for product differentiation 

and unique styling has pushed the performance 

envelope of traditional materials. At 

the same time, the demands of the marketplace 

continue to reign in costs and design development 

time. 

GE Plastics, an engineering resin supplier, has 

used FLUENT software to deliver more precise 

material selection guidelines to their automotive 

customers by predicting the operating temperature 

of a given headlamp reflector design. According 

to David Bryce, GEP’s Technical Manager, 

Lighting, “By selecting the most appropriate material 

for each component, our customers can design 

for tooling and manufacturing needs that are 

specific to that material. Additionally, the lowest 

cost material meeting the thermal load requirements 

can be chosen, averting costs due to 

over-engineering of the design.” Design-specific 

heat transfer and fluid flow analysis captures the 

uniqueness of each lamp system. 

Development timelines are continually being 

shortened however, and the time required to model 

and analyze a complex system can sometimes 

extend the entire program timeline. “We are finding 

that many of our customers can only allow 

very little time in their design cycle for feasibility 

analysis,” says Jim Wilson, GEP’s Commercial 

Technology Manager, High Performance Polymers. 

A completed design must be immediately sent 

out for tooling prior to verification that the correct 

material selection was made. Iterative design 

changes are viewed as inefficiency in the 

process. A material choice is needed to optimize 

the design, yet the appropriate material cannot 

be selected until the design is ready for analysis 

and its thermal requirements determined. This 

conundrum has encouraged GEP to implement 

FLUENT in the design process in a new way. 

GEP wanted to put the ability to design in 

its customers’ hands, rather than dictate changes 

to suit material requirements after the design was 

finalized. To accomplish this, they developed a 

headlamp design calculator to assist their customers 

in making up-front material selections. 

Using FLUENT, GEP was able to examine a broad 

array of common lamp designs and focus on the 

design features that were most critical to material 

selection. The result was a design calculator, 

soon to be available to GEP’s customers on their 

An example of an automotive head lamp reflector 

web site www.geplastics.com. The calculator 

allows the customer to examine their allowable 

system space before they ever produce any design 

data. Instant temperature and material suggestions 

enable them to adjust or trade-off various 

elements of their design to achieve a more costeffective 

material specification. This is all prior 

to establishing a final geometry that can then 

be optimized for that material. 

A verification of correct material selection is, 

of course, needed when the design is finalized. 

At that point a design-specific CFD analysis can 

be performed, but the initial material suggestion 

from the calculator helps reduce post-design 

modifications and speed development. ■ 

The automotive lighting design calculator 


plastics 

The use of thin flexible films for the packaging of 

disposable sterile medical devices is a large and 

growing part of the medical packaging market. 

In most cases, the packaging format used for medical 

devices is a formed pack produced using a thin 

polymeric film sealed to a top web of paper, which 

permits the ingress of the sterilization gas but is resistant 

to bacterial penetration post sterilization. In addition, 

to keep the cost of the packaging to a minimum 

and to reduce environmental impact, it is desirable to 

use as thin a polymeric web as possible. In the case 

of a syringe pack, the film thickness may typically be 

65 – 150µ, reducing to as low as 15 – 35µ in the corners 

after the thermoforming process. This is adequate 

for providing a sterile environment, but may not be 

sufficiently rugged for the life and demands of the packaging. 

For instance, during transit from the manufacturing 

site to the end user, it is important that the package 

remains intact with no holes or pits forming in the film. 

A small hole of 10 microns will allow airborne bacterial 

spores to ingress into the pack, leading to a sterilization 

failure. 

At REXAM, one of the top consumer packaging companies 

in the world, transit tests have been devised 

to simulate and quantify levels of packaging failure for 

syringe packs. The rates of failure typically average less 

than 0.2%, with the two primary causes being abrasion 

and puncture by the syringe. Failure due to puncture 

was of primary interest to REXAM engineers. They 

wanted to develop a technique to predict failure accurately 

and use this knowledge to “reverse engineer” 

their packaging, so that it would be less prone to puncture. 

The approach they chose involved two computational 

software packages: POLYFLOW, to model the 

thickness distribution of the thermoformed pack; and 

MSC.Marc, a stress analysis code, to model the strain 

rate of the thermoformed packaging and predict probabilities 

for puncturing the pack. When combined, these 

two simulation techniques could be powerful predictors 

of mechanical strengths for a given type of syringe packaging. 

REXAM engineers validated their modeling approach 

for a typical 10ml syringe package using two different 

film packaging materials. Both films were thermoformed 

into a “coffin” style die for the 10ml syringe. 

In the experimental tests, randomly chosen packs were 

punctured using a Lloyd Tensile Tester. CFD models 

for the two cases were set up in POLYFLOW, using the 

physical properties, including the special rheological 

behavior, of each material used. A membrane approximating 

approach was used to simulate the thermoforming 

process in order to reduce the computational 

time required. The CFD predictions were in excellent 

agreement with measurements for one of the films, 

and in good agreement for the other. The puncture 

resistance simulations using MSC.Marc were also in 

very good agreement with measurements, thus confirming 

the suitability of this dual simulation approach 

for analyzing this type of film packaging. REXAM believes 

that the ability to assess material changes in the packaging 

design will lead to significant time and cost savings 

in their manufacturing processes in the future. ■ 

Typical syringe and packaging 

Preventing 

Punctures in 

Sterile Packaging 

by Roy Christopherson, REXAM Flexibles Ltd., Bristol, England 

POLYFLOW CFD simulation of the 

“coffin” thermoformed product 

packaging, showing the thickness 

distribution. Predictions for thickness 

at five locations on the coffin surface 

were found to be in very good 

agreement with experimental 

measurements for both materials 

tested. 



S5

semiconductor 


Optimizing 

Photo-Resist 

Film Uniformity 

by David Crowley, Tokyo Electron Texas, Inc., 

Austin, TX 

Flow characteristics within the exhaust cup 

Tokyo Electron Texas, Inc., (TEX) is part 

of a worldwide organization, Tokyo Electron 

Limited (TEL), a leader in semiconductor 

and LCD production equipment based in 

Japan. TEX performs research and development 

for Tokyo Electron’s Clean Track systems, 

which dominate the market because of their 

reputation for superior reliability and performance. 

Clean Track systems are used in the photolithography 

process that silicon wafers undergo 

during microchip fabrication. They are used 

to coat silicon wafers with a sub-micron thick 

layer of photo-sensitive polymer (called 

photo-resist), perform baking and surface preparation 

processes, send the wafers to a pattern 

exposure tool, and develop the photo-resist 

after exposure. The precision of the resulting 

pattern is strongly dependent on the uniformity 

of the photo-resist thickness across the 

wafer. This thickness is governed by the wafer 

rotation speed and air flow inside the system, 

which is driven in part by the design of an 

exhaust cup, used to remove volatiles. To understand 

the features of two different exhaust cup 

designs, two models of about 1.4 million cells 

each were analyzed using FLUENT. 

The FLUENT results were in agreement with 

a simulation done previously by TEX’s parent 

company, Tokyo Electron Kyushu (TKL), 

which used slightly different boundary conditions 

and other software tools. The results 

supported observations of vortices created at 

high spin speeds, giving engineers confidence 

in the simulation techniques and providing 

valuable information related to the modification 

of the exhaust cup to improve the system 

performance. In the future, FLUENT will 

be used to verify improvements to the airflow 

and exhaust system designs. ■ 


Accelerate Promising Flat Panel 

Display Research 

by Tolis Voutsas, Sharp Laboratories of America, Camas, WA 

Sharp Laboratories is the worldwide leader in the development and mass-production of flat 

panel displays, otherwise known as thin film transistor LCDs, or TFT-LCDs. Recently there 

has been an explosive growth of low-temperature polycrystalline silicon (poly-Si) TFT technology 

that promises to deliver novel, high performance, high-content displays. The new concept 

of a “sheet-computer,” where the display is the heart of the system, offers multiple functions 

(input/output, data/video imaging, etc.) on a very thin and portable device. For such concepts 

to materialize, the development of new process technology is needed to understand the complex 

interactions between individual process parameters. Sharp Labs of America is focusing on 

the development of such new processes, equipment, and materials to advance the state of LCD 

technology. 

One area of particular interest and complexity is the crystallization of amorphous silicon to 

form poly-Si films. The quality of the poly-Si microstructure impacts the performance of devices 

made with these films and profoundly affects the display capabilities. FIDAP has been used to 

simulate the transformation of amorphous-Si thin films to poly-Si through irradiation of the former 

by a pulsed laser beam. This is a highly complicated process in which the thin film experiences 

ultra-rapid heating, melting, and equally rapid cooling. The process is complicated by several 

factors: phase change occurs far from thermal equilibrium; nucleation occurs in the molten material 

as it cools, and the crystals grow and coalesce. A number of modifications have been implemented 

in FIDAP through user-defined subroutines to incorporate these complexities into the 

existing phase change model. 

Equipped with this advanced simulation tool, the temperature history in the film as a function 

of the relevant problem parameters can be computed, and the final microstructure within 

the laser-irradiated area can be predicted. The predicted microstructure has been found to compare 

favorably with images of the actual material obtained experimentally. As a result, FIDAP 

has been used as a reliable substitute for experimental work to identify promising operating regimes 

that optimize the material properties (microstructure). In addition, simulation has been used to 

investigate different irradiation schemes that are either difficult or expensive to implement experimentally, 

unless sufficient evidence exists to warrant the value of the expenditure. As new features 

have been added to the model, the value of accurate simulation has proved to be invaluable 

in the investigation of these highly complex processes. A vast array of operating regimes can now 

be explored without having to resort to tedious and time consuming traditional methods. ■ 

temperature (K) 

3000 

2800 

2400 

2000 

1600 

1200 

800 

400 

0 

0.00 

Si 

SiO2 

laser pulse 

substrate 

0.05 0.10 0.15 0.20 

Temperature history at various locations 

within the film stack 

Position and temperature of the solid-liquid interface 

within the top Si layer as a function of elapsed time 

Comparison of simulated (left) and experimental (right) poly-Si microstructure for 

the case of laser irradiation that results in random nucleation at the center of the 

irradiated domain, a scenario that is typically undesirable 

interface position (µm) 

4 

3 

2 

1 

0 

0.00 

interface temperature 

interface position 

0.05 0.10 0.15 0.20 

0.25 0.30 

1800 

1700 

1600 

1500 

1400 

1300 

interface temperature (K) 


semiconductor 



by Keyvan Keyhani and Sameer Abu-Zaid, Asyst Technologies, Inc., Fremont, CA 

Asyst Technologies, Inc. is the 

world’s leading provider of 

environmental and automated 

work-in-progress material 

management systems for the semiconductor 

manufacturing industry. 

For the fabrication of integrated circuits, 

Asyst’s automated wafer isolation 

solutions enable the safe and 

rapid transfer of wafers between 

process equipment and the fabrication 

line, thus increasing production 

yield and reducing operating 

expenses. At Asyst, CFD analysis is 

used for design optimization during 

product development, performance 

verification of existing 

systems, and troubleshooting of contamination 

problems. CFD has 

proven to be a valuable tool in the 

design and analysis of a broad range 

of environmental isolation systems. 

As an example of the value of 

CFD analysis at Asyst, FLUENT has 

been used to optimize nitrogen purg- 

Geometry of the CFD model. The front surfaces of the FOUP 

have been removed to display the wafers. The inlet and 

outlet ports are shown on the bottom in blue and green, 

respectively. 

ing of a 300mm front opening unified 

pod (FOUP). Vapors inside FOUPs 

can damage wafers during transport, 

storage, and queuing between 

processes. For example, moisture 

can cause native oxide growth, corrosion, 

and cracking of films, and 

contamination by various organic 

compounds can degrade the electrical 

properties of integrated circuits. 

Purging with an inert gas, such 

as nitrogen, is a method of removing 

harmful vapors from FOUPs. 

To determine optimal purging 

methods, a CFD model of the system 

was developed. A FOUP 

geometry filled with 25 wafers was 

first constructed using Pro/E, and 

the model was imported into 

GAMBIT for meshing. The FOUP was 

initially set to contain air (with 20.7% 

oxygen). Pure nitrogen was then 

injected through inlet ports on the 

bottom of the FOUP, and the transient 

change in vapor concentration 

was computed. Various injection 

and exhaust methods were simulated 

using the same total amount 

of nitrogen for all cases, to determine 

the fastest rate of oxygen 

removal in all regions of the FOUP. 

Examination of a series of oxygen 

contour plots on the center 

plane of the FOUP show that the 

average concentration of oxygen 

drops rapidly over time, and that 

regions between the wafers can be 

effectively purged within an acceptable 

time period. Plots of oxygen 

concentration vs. time between two 

wafers show good agreement 

between FLUENT predictions and 

experiment. Using CFD as a predictive 

tool for purging optimization 

is less expensive than 

experimentation and provides 

Contours of mass percent of oxygen on a plane through the 

wafer centers after 40s of purging (not the optimal purge 

results). The FOUP initially has 20.7% oxygen (red) in every 

region. 

% oxygen 

100 

10 

1 

0.1 

0.01 

0 40 80 

FLUENT 

Measurement 

120 160 200 240 

time (s) 

Comparison of FLUENT results and data at the center point 

between the top two wafers (not the optimal purge results) 

detailed concentration results in every 

location within the FOUP. 

Work is ongoing at Asyst to further 

improve the purging of FOUPs 

using FLUENT simulations. Asyst is 

also presently using FLUENT for 

design optimization of the next generation 

of ultra-clean mini-environments 

for automated 300 mm 

wafer handling. ■ 



S7

metallurgy 


Steel Industry 

Applications at 

ARCELOR 

by Jean-Francois Domgin and Pascal Gardin, IRSID, ARCELOR, Maizieres les Metz, France 

Process chart 

vertical velocity [m/s] 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

-0.1 

Ladle 

-0.2 

0.0 0.2 0.4 

r/R 

Continuous 

casting mold 

experimental results 

Eulerian approach (FLUENT 4.5) 

Lagrangian approach 

0.6 0.8 1.0 

Fluid velocity for a water scale model of a ladle – FLUENT 

results compared to experimental data 

The manufacture of steel products is a complex 

process. Demands for improved product 

quality have led research centers 

dedicated to the steel industry to try to better 

understand all phases of the manufacturing 

process. Improved measurement techniques 

and numerical simulation are two of the many 

areas where the efforts have been directed. 

At ARCELOR, Europe’s largest steel producer 

and one of the leading steel producers in 

the world, FLUENT has been used for 

numerical simulations of various steel production 

processes, many of which involve multiphase 

flow. The work has been carried out at IRSID, 

the company’s central research organization. 

purging in a ladle 

Steel ladles are used for the transport of 

molten steel to product forming stations, temporary 

holding prior to the forming operation, 

chemical addition, and purging. 

Chemical addition is done to give the steel 

the required properties, and purging, usually 

with jets of argon gas, is done to homogenize 

the mixture, both thermally and 

chemically. It is also used to promote the upward 

motion of inclusions, undesired particulate 

matter that develops when certain substances 

are added. A slag, or layer of impurities, forms 

on top of the molten metal, and by transporting 

the inclusions to the slag layer, they 

can be removed with the slag prior to product 

forming. Both the discrete phase model 

(DPM) and the Eulerian multiphase model 

have been used to simulate the purging process, 

and results are in good agreement with PIV 

measurements on water scale models. 

decarburization in a 

vacuum degasser 

Vacuum degassing is another process that 

is used to purify molten steel. The steel is drawn 

up from the ladle through a snorkel into a 

vessel held at high temperature and low pressure, 

an environment that helps remove 

unwanted carbon and dilute gas from the melt. 

The upward flow is driven by the injection 

A vacuum degasser, showing the two snorkels at the bottom 

of argon or oxygen gas. The steel is returned 

to the ladle through another snorkel after the 

degassing and decarburization have occurred. 

FLUENT has been used at ARCELOR to simulate 

the flow field induced by the gas injections, 

and to study the tracks of carbon and 

gas particles in the degasser. 

cleanliness in 

continuous casting 

The continuous casting process has been 

studied carefully because it is critical to the 

final product quality. Casting is most successful 

if there is a gradual yet steady growth of the 

solidified shell, with few or no inclusions trapped 

in the material. An understanding of the flow 

patterns in the casting mold is therefore very 

important, since it is an indicator of the inclusion 

behavior and can be used to evaluate 

the effects of argon injection mechanisms and 

electromagnetic actuators. The effect of argon 

injection can be simulated using either the 

DPM or Eulerian multiphase model. Inclusions, 

on the other hand, are best modeled using 

the DPM, since it more conveniently allows 

for a range of particle sizes and densities. 

Electromagnetic fields have been incorporated 

into the FLUENT simulations using a module 

developed at the EPM-MADYLAM 

Laboratory in Grenoble. The module includes 

a Lorentz force term in the momentum equations 

for the melt and particles that has been 

found to contribute not only to the flow patterns 

and particle trajectories, but to the deformation 

of the free surface as well. ■ 

Behavior of inclusions injected into a continuous 

casting mold 


HVAC 

Smoke Management 

at Frankfurt Airport 

by Ingo Cremer, Joachim Luy, Jens Elmers, and Albrecht Gill, Fluent Germany 

An overview of the buildings at Terminal 1 

In Germany, the occurrence of one severe fire accident 

at an airport has led officials to review the existing 

fire protection strategies for airport buildings as 

well as those for renovated terminals. Thus when the 

renovation of Terminal 1 at Frankfurt Airport was planned, 

fire protection scenarios had to be checked and possibly 

optimized. In order to compare the performances 

of different concepts, FLUENT simulations of the original 

geometry of the terminal were ordered by the airport 

authorities. For validation purposes, experiments 

using a 1:20 model were performed. 

The effort began with simulations of the external air 

flow around the buildings that make up the terminal. The 

results were used to predict static pressures along the outer 

surfaces of the buildings and at several potential building 

openings. A second set of simulations focused on fire 

management inside the departure hall of Terminal 1. Of 

particular interest was the time-dependent dispersion 

of smoke using different combinations of ventilation fans 

and openings. Results from the external simulations were 

used to identify the optimum locations for fresh air supplies 

for the fire scenarios. 

For the external flow simulations, GAMBIT and TGrid 

were used to build a hybrid mesh of about 4.9 million 

cells, based on engineering drawings of the airport buildings. 

This model spans a geometric region of 2830 x 

2830 x 500m 3 . Surrounding the building of interest, a 

typical mesh size of 0.9m was used. An exponential profile 

for the wind velocity as a function of height above 

the ground was used as a boundary condition. Two wind 

conditions were considered: one blowing from the 

Northeast at 3.7 m/s (8 mph), and one from the Southwest 

at 5.4 m/s (12 mph). The simulations were performed 

using the parallel version of FLUENT. All of the external 

flow results, even the pressure levels on the building 

surfaces, were successfully validated through 


HVAC 

measurements on the scaled structure. 

Because of a tall building adjacent to (and south of) 

the departure hall, the pressures on the roof of the departure 

hall were found to be different for the different wind 

conditions. This important realization made it clear that 

the smoke management had to be based on a combination 

of fans and natural smoke outlets, rather than 

on outlets alone. Fans ensure consistent smoke extraction, 

independent of exterior weather conditions that 

might compromise the efficacy of the outlets. 

The second phase of the project involved an examination 

of the flow field inside Terminal 1 itself, with the 

primary goal being the optimization of the smoke management 

system in the departure hall. A mixed concept 

of mechanical and natural ventilation systems was 

tested. The internal geometry was again created in GAM- 

BIT based on engineering drawings of Terminal 1. Most 

of the meshing was done in GAMBIT as well, while TGrid 

was used to assemble the meshed parts into a whole. 

The resulting mesh had 1.3 million cells. To have the 

flexibility of placing trial outlets where needed, this model 

was equipped with openings in many locations. For each 

simulation, the inactive outlets were switched to walls 

in FLUENT. The calculations were again performed using 

the parallel solver. 

All fire simulations are inherently unsteady. Taking 

into account the flow physics, safety requirements, and 

flow handling devices typically used for fire prevention 

tasks, a sophisticated time dependent control system 

was developed. At t=0, the fire is assumed to begin. 

After one minute, it is detected, and after another minute, 

the smoke outlets are activated. Three minutes after the 

fire begins the extinguishing system is activated and after 

ten minutes, the fire fighters arrive on the scene. 

For the indoor simulations, fires at five different locations 

were set up following the guidelines of a fire pro- 

Static pressure on the outer surfaces of the buildings during 

northeast (above) and southwest (below) wind conditions. The 

tall structure at the center alters the pressure on the roof of the 

adjacent departure hall for the different wind conditions. 


HVAC 

The geometry of the internal model (left) 

showing some of the grid detail (right). The 

departure hall is the area colored green in 

the geometry. 

tection expert. The fires were modeled as transient sources 

of hot smoke in FLUENT with a number of simplifying 

assumptions. Most of the fire simulations were run for 

a physical time of 8 minutes, using a time step that ranged 

from 0.2 to 4 seconds. In spite of the simplifications 

made, all of the simulations showed good agreement 

with experimental measurements from the scaled 

1:20 model. 

Several optimization runs were performed for the different 

fire locations. During this phase of the project, it 

became evident that dividing the hall volume into active 

smoke management segments had a very positive effect 

on the smoke exhaust, because the fans were loaded with 

the nearby smoke and not air. In contrast, attempts to 

dilute the smoke with air had a negative effect. The contaminated 

volume merely grew more rapidly and, as a 

consequence, more fans with a given volume flow were 

needed to carry the smoke-air mixture out of the hall. 

In addition to segmenting the hall, attempting to 

create a layer of smoke in the upper region while keeping 

air in the lower region of the hall was found to be 

advantageous, especially near the escape routes. In order 

to achieve this, the mixing of smoke and air had to be 

suppressed and a stable stratification of gases had to 

be achieved with a well-chosen combination of ventilation 

fans and building openings. To achieve this goal, 

it was found that windows should not be opened in 

the wrong places, and that fresh air supplies in general 

should be large enough and far away enough to 

avoid unwanted mixing. 

In the course of the project, several parameters were 

modified as the five different fire locations were independently 

studied. Special care was taken for regions 

with low ceilings, where it was more difficult to create 

and maintain a thin smoke layer well above the floor. 

Properly positioned fans and smoke outlets were critical 

for keeping a nearly smoke-free layer, about 2m 

thick, on the floor, to allow people to escape safely. 

Based on the experimental and CFD results, the airport 

management is able to judge renovation measures 

beforehand in order to maintain a high level of 

airport security. ■ 


HVAC 

Improving 

Air the for 

Arias 


HVAC 

by Tamás Régert, Gergely Kristóf, Tamás 

Lajos of Budapest University of Technology 

& Economics, Budapest, Hungary; 

and Atul Karanjkar, Fluent Europe 

Budapest, the capital of Hungary, is 

one of the most beautiful cities in the 

world. One of the jewels in its architectural 

crown is the Budapest Opera House, 

built by Miklós Ybl in 1884 at the height 

of the Austro-Hungarian Empire. It has several 

ornate decorations that are stunning, 

and like many public buildings of its vintage, 

was designed with natural ventilation 

in mind. One ventilation feature, for 

example, is a central chimney above a large 

chandelier that hangs from the ceiling. 

When a Fluent Europe staff member 

visited the local Fluent partner in Budapest 

last year, the two went to the Opera House 

to see Tchaikovsky’s Eugen Onegin. It was 

a hot day in May, and both felt that the 

building was quite warm during the performance. 

The experience inspired them 

to contact the technical manager of the 

Opera House and introduce him to 

Fluent’s CFD software, a tool that could 

help find a solution to the building’s thermal 

comfort control problem. 

At the beginning of the last century the 

Opera House’s natural ventilation system 

relied on drafts that were governed by the 

temperature differential across the central 

chimney. This meant that higher temperatures 

were needed in the upper reaches 

of the building in comparison to the 

cooler temperatures outside. The natural 

drafts acted to draw air up and out of the 

auditorium. Vents underneath the seating 

area could be opened to permit air to flow 

into the auditorium, if needed. During the 

summer months, the incoming air passed 

over ice blocks to provide additional cooling. 

In the past decades the ventilation 

system was modernized several times, with 

the last upgrade occurring in the 1980s. 

During the renovations, forced ventilation 

and air conditioning systems were introduced, 

and the stage was outfitted with 

a separate air conditioning system, which 

prevents cross-flow between the auditorium 

and stage. 

Fluent’s Hungarian partner decided to 

offer the manager of the Opera House a 

free HVAC assessment of their building, 

with the goal of identifying hot spots dur- 

ing a typical performance. The CFD simulation 

encompassed the whole auditorium 

(minus the stage) with the simulated 

effect of an audience of 1250 heat-generating 

people and the lights dimmed. The 

realizable k-ε turbulence model was used 

in the study and full buoyancy effects were 

included. The CFD results showed that the 

orchestra pit ventilation was poor in places, 

a fact that musicians all too readily confirmed 

from their own experiences. 

Thermal anomalies in the balconies were 

also correctly identified. 

This is not the first time that consultants 

have helped the Budapest Opera House. 

Fifty years ago, the celebrated scientist Leo 

Beranek, an expert in the field of acoustics, 

carried out a sound characterization of the 

building. His data is still in use today. Fluent’s 

CFD study will be used in an upcoming 

reconstruction and modernization of the 

Opera House, which will include an overhaul 

of its air conditioning system. Once 

the renovations are complete, opera enthusiasts 

in Budapest will no doubt be appreciative 

of Fluent’s CFD efforts for the next 

fifty years! ■ 

Path lines, colored by temperature, show differences of up to 

5°C throughout the seating area and orchestra pit 

A view from the stage of the simulated auditorium, showing 

temperature contours 


automotive 

Customized 

Phosphate Dip Tanks for Cars 

by Christof Knüsel, Dürr Systems GmbH, Paint Systems Automotive, Stuttgart, Germany 

As a full-range systems supplier, 

Dürr Systems GmbH offers 

turn-key paint shops for 

mass production paint finishing. The 

complete package contains buildings, 

plant and environmental engineering, 

conveyor equipment and control, 

automation, and material 

handling techniques. Dürr also 

offers a complementary range of manufacturing 

support services for all 

aspects of the paint finishing process. 

One important component of their 

work involves CFD simulations. 

Since 1998, Dürr has used FLUENT 

to model such things as air flow in 

spray booths and work stations, air 

flow and heat transfer in ovens, mist 

elimination in scrubbers, response to 

electric fields during cathode dip painting, 

and fluid flow in dip tanks. 

Pretreatment is the first of many 

stages in the painting process. Here 

the automotive body is cleaned and 

prepared for subsequent coating 

processes using methods appropriate 

for the material involved (steel, 

aluminium, magnesium, etc). One 

phase of the pretreatment process, 

called phosphating, is used to apply 

a zinc phosphate base coat. The 

process is normally carried out in dip 

tanks, where the flow is driven by 

100 to 300 injection nozzles. This coat 

protects the body from corrosion and 

acts as a bonding base. A secondary 

reaction produces iron phosphate, 

which takes the form of sludge and 

is removed from the dip tank continuously. 

When the process is applied to 

aluminium sections, it triggers a further 

secondary reaction, which produces 

cryolite. To counteract any 

reduction in surface quality arising 

from the presence of cryolite, the flow 

velocity should always be above 0.3- 

0.5 m/sec near the aluminium 

components. Since the current 

trend is toward bodies with more aluminium 

parts, phosphate tanks in exist- 


automotive 

ing plants often need to be upgraded 

for new car models in order to 

increase the flow velocity at critical 

points. 

CFD simulation is an excellent tool 

for optimizing the flow in a phosphate 

tank. First, a simulation of one 

injection nozzle is carried out using 

a fine grid of approximately 150,000 

cells. The results are used to generate 

velocity and turbulence profiles 

that are characteristic of the nozzle. 

Second, a simulation of the complete 

tank is performed using a larger (about 

2 million cells), yet comparatively coarser 

grid. The velocity and turbulence 

profiles predicted in the first simulation 

are used as boundary conditions 

for the injection nozzles in the 

second. The profiles are modified 

slightly to ensure that the jet characteristics 

on the coarser grid are nearly 

identical to those on the fine grid 

of the first simulation. The second 

round of calculations usually requires 

several days to obtain a suitably converged 

solution, using a 1.0 GHz 

processor. Experiments using tanks 

filled with water show good agreement 

with the simulation. 

CFD has enabled Dürr to develop 

new dip tanks with optimized flow 

conditions and offer customers individual 

solutions for optimizing existing 

tanks to suit new car models. The 

3D simulation plots make it easy for 

customers to understand where the 

problem areas lie, and where modifications 

should be made to obtain 

a better surface quality. A decline in 

surface quality can result from poor 

flow in dip tanks, and can add expense 

to customers’ operating costs. In many 

cases it can be avoided with CFD. ■ 

Detail of injector nozzle jet simulation 

Detail simulation of injector nozzle jet 

Flow in a dip tank: the side-flooding 

system is illustrated by path lines 


automotive 

Arrows 

Formula 1 

Team 

Moving Up 

The Grid 

by Peter Machin, Senior CFD Engineer, Arrows Formula One Team, Oxfordshire, UK 

The Formula One racing calendar consists of 17 grueling races across 

four continents. The only goal of each team is for its car to win. The 

teams are focused on building racing cars to compete at the pinnacle 

of motorsport. There are many factors that must be considered in designing 

such a highly complex machine. The designers’ aim is to make the car 

the fastest around every corner, on every lap. Driver ability, weather conditions, 

and luck play a part, but the performance of the car is paramount. 

In recent years, the Arrows Formula One Team has used FLUENT to maximize 

performance. The majority of our team’s work has been in the area 

of wing design, with a particular focus on assessing the level of downforce 

and its effect on the performance of the car. Over half of the car’s total 

downforce is due to the wings. However, the production of downforce comes 

with an associated drag force penalty. The aim of the designer is to find 

wings that generate more downforce with a minimum increase in drag, 

which on the racetrack could mean the difference between a place on the 

podium or not. 

Without CFD, many more wing prototypes would have to be constructed 

and tested, which would be very time consuming and expensive at a time 

when the focus is on ever-shortening design cycle times. Before CFD, all 

F1 wings were very similar and based on ground-effect wing profiles published 

in the public domain. Thanks to CFD, designers can predict exactly 

which performance improvements will accompany every wing shape 

modification, no matter how subtle. Indeed, we can usually expect better 

than 90% accuracy on wing element forces before putting them in the wind 

tunnel. FLUENT has also been used to provide accurate load data for our 

stress department, and forces on other parts of the car. 

One important issue that all Formula One teams must focus on is safety. 

Ultimately, safety comes first and the Fédération Internationale de l’Automobile 

(FIA) has laid down clear rules to which each team must adhere. Each race 

season, CFD is used to optimize the racing car design within the FIA regulations. 

In recent years, it has been particularly useful in reacting to aerodynamic 

rule changes that have further limited the size and number of wing 

elements on the car to reduce cornering speeds. 

At Arrows, our long-term objective with CFD software is to carry out 

much more detailed full-car work. We regularly do simulations of 10 million 

cells and plan to expand to 30 million cells in the near future as we 

tackle larger problems and look at existing ones with finer mesh resolution. 

It is our belief that as FLUENT becomes more widely used on such 

areas as radiator cooling flows, it will become as common as CAD on the 

designer’s desktop computer. ■ 


automotive 

External flow around the body and inside the engine bay 

of the Bentley Arnage: contours of static pressure and path 

lines colored by velocity magnitude 

For the Driver Who 

Has Everything 

by Keith Hanna, Director of Marketing Communications, Fluent Inc. 

Project done in collaboration with MSX International Ltd. and Bentley Motors 

With 835 Nm of torque, 400 bhp 

(brake horsepower) and a 6.75 

liter V8 engine, the elegant 

Bentley Arnage is an English luxury automobile 

from the old school. Hand-built 

in Great Britain by Bentley Motors of Crewe, 

now part of the German Volkswagen Group, 

these luxuriously appointed, individually 

tailored cars are the most powerful 

Bentleys ever, capable of speeds up to 155 

mph with an impressive acceleration of 

0 – 60 mph in just 5.9 seconds. In addition 

to its awesome engine, the Bentley 

Arnage is noted for its outstanding ride 

characteristics, minimal internal noise, speedsensitive 

steering, and spacious interior. 

Recently, Bentley has married the ageold 

tradition of English engineering 

craftsmanship to advanced technology to 

further perfect its designs. This effort has 

resulted in state-of-the-art suspension, transmission, 

braking systems and, thanks to 

CFD simulations performed by Fluent 

Europe and MSX International, improved 

aerodynamics. MSX is a British firm that 

provides engineering solutions for its clients. 

After evaluating a number of CFD codes, 

they selected FLUENT to simulate the external 

flow around the Bentley Arnage. The 

choice was based on FLUENT’s ease-ofuse 

and flexible meshing capabilities. 

The CFD work made use of a centerline 

symmetric model created with 

detailed underhood and underbody resolution 

that was subsequently used in a 

benchmark study. The FLUENT simulation 

was adapted to values of y+ and gradients 

of pressure during the convergence 

process, and the final CFD predictions 

agreed well with experimental measurements. 

Engineers from MSX and 

Bentley look forward to using CFD to tackle 

future challenges in the design and optimization 

of the next generation of elite 

cars. This automotive manufacturer is clearly 

setting the pace at the top end of the 

automotive market in more ways 

than one. ■ 

Path lines 

colored by 

velocity 

magnitude 

showing the 

flow pattern 

near the 

underbody 

of the 

Bentley 

Arnage 



The Power of SOFC 

Fuel Cells 

by Mehrdad Shahnam and Michael Prinkey, Senior Consulting Engineers, Fluent Inc. 

The geometry of the tubular SOFC shows the interconnects in 

green (used to electrically connect a stack of fuel cells), the air 

inflow and oxidizer channel in red, and the electrolyte in blue. 

The anode and cathode are cylindrical surfaces on the inside 

and outside of the electrolyte, respectively. 

exterior 

fuel flow 

electrolyte 

interconnect 

support tube 

oxidizer channel 

Temperature contours on the cathode 

side of the electrolyte 

air flow in 

Fuel cell technology promises to provide an environmentally 

friendly source of power with broad applications in many 

industries, such as transportation and the military. Among 

the current issues surrounding the continued development 

and deployment of this technology is that of manufacturing 

costs. Reduction of manufacturing costs can only be realized 

by optimizing the efficiency of the devices, and this can 

only happen through detailed analysis of the complex electrochemical 

and mass transport phenomena taking place. 

Fluent has developed modeling tools for FLUENT to help 

meet this need, so that engineers can optimize fuel cell design 

as well as performance. (See the Partnerships section on page 

42.) As part of this ongoing effort, a user-defined function 

(UDF) has been developed recently with detailed models 

for Solid Oxide Fuel Cells (SOFC), a variety that is being targeted 

for distributed power applications, portable power generation 

(for the military), and auxiliary power units (or APUs, 

for commercial aircraft). 

The SOFC module works in tandem with a FLUENT calculation 

that includes species transport and heat transfer. 

Species and temperature fields are passed to the SOFC model, 

which uses them to compute the current density, cell voltage, 

and heat flux at the electrodes. This information is then 

passed back to FLUENT, where it is used to update the species 

and temperature fields. The process continues in an iterative 

manner until convergence is reached. In addition to the 

fuel cell geometry, the operating characteristics include the 

total current output for the fuel cell, which is set as an initial 

condition. The comprehensive SOFC model, which is fully 

parallelized, address the following important processes: 

electrochemistry 

Appropriate chemical reactions for H 2 and CO are used 

to predict the local current density and voltage distributions 

at the electrolyte surfaces. The electrolyte layer is assumed 

thin for electrochemical modeling purposes (the ionic transport 

across the electrolyte is assumed to be one dimensional), 

but a finite thickness region in the FLUENT simulation can 

be used to represent it. The electrochemical model takes 

into account the losses due to activation overpotential (kinetic 

losses), ohmic overpotential (losses due to ionic transport 

in the electrolyte), and concentration overpotential (losses 

due to to inadequate diffusion of species through the electrodes). 

Binary diffusion coefficients are used to calculate the 

molecular diffusion of the (gaseous) species throughout the 

domain. 


The current density and voltage on a 

surface through the electrolyte 

potential field 

This model predicts the current and voltage in all conducting 

solid and porous regions of the SOFC. Heat generated 

as a result of ohmic losses in the conducting regions 

is also predicted. 

The model has been applied recently to a tubular SOFC, 

where hydrogen and air are used as the fuel and the oxidizer, 

respectively. Fuel utilization is about 80% and the oxidizer 

utilization is about 25%. The total cell current is 11 

Amp and the average current density is 1850 A/m 2 . The figures 

illustrate the non-uniformity in several of the fuel cell 

variables that could not be captured by a more simplified 

approach. ■ 

more.info@ 

fuelcells@fluent.com


The 3D hybrid mesh used for all simulations 

Researchers at the Combustion Technology Section 

of ENEA (Research Center La Casaccia) in Rome 

recently validated FLUENT through a series of simulations 

of the WS Rekumat C-150 B burner. Operation 

of the 40 kW burner is based on flameless combustion 

technology 1 , which gives rise to high process efficiency 

with low pollutant emissions. While the burner is designed 

to operate in either conventional flame or diluted combustion 

(flameless) modes, only the latter was the subject 

of the present studies. Measurements were made 

for three sets of operating conditions, corresponding 

to process temperatures of 950, 1050, and 1150 K. These 

were compared to results predicted by FLUENT for the 

corresponding conditions. 

In FLUENT, two different combustion modeling 

approaches were tested: the mixture fraction/pdf method, 

using an equilibrium assumption, and the Magnussen, 

or finite rate/eddy dissipation method, using a one-step 

reaction mechanism. For both sets of simulations, NO x 

prediction was performed. The realizable k-ε turbulence 

model was chosen to give the most accuracy for the 

least amount of CPU effort, based on earlier benchmark 

tests performed for similar conditions. Radiation was incorporated 

through the use of the discrete ordinates (DO) 

model. 

Both the pdf and Magnussen models gave good qualitative 

agreement with the experimental data, with the 

Magnussen model outperforming the pdf model in its 

prediction of centerline temperatures for the low and 

moderate process temperature cases. This result suggests 

that at these temperatures, the diluted combustion 

is controlled more by kinetics than by turbulent mixing. 

The equilibrium assumption at the core of the pdf model 

fails to accurately predict the ignition delay in this regime. 

At the highest process temperature, the ignition delay 

is reduced. The Magnussen model overpredicts the delay 

as well as the maximum temperature. The pdf model, 

on the other hand, comes closer to predicting the overall 

temperature field, even though the maximum temperature 

is again higher than that suggested by the 

measurements. This result suggests that turbulent fluctuations 

in the local temperature and mixture fraction, 

which are better handled by the statistical methods of 

the pdf model, play a more important role in this regime 

of operation. Temperature fluctuations were found to 

play a significant role in thermal and prompt NO x production 

at the higher temperature, as well. ■ 

Flameless 

Burner 

Validation 

by Daniele Tabacco and Claudio Bruno, University of Rome La Sapienza - 

Department of Mechanics and Aeronautics, Rome, Italy; and 

Giorgio Calchetti and Marco Rufoloni, Italian National Agency for New Technology, 

Energy, and the Environment (ENEA), Rome, Italy 

The measured temperatures for the 1050 K reference temperature case 

The temperatures predicted by FLUENT for the 1050 K reference 

temperature case, using the one-step Magnussen model 

reference 

1 Wunning, J. A., and Wunning, J. G., Burners for Flameless Oxidation 

with Low-No x Formation Even at Maximum Preheat, Journal of the 

Institute of Energy 65, 35-40, 1992. 


product news 

New Specialty 

Modules for 

FLUENT 6.0 

by Nicole M. Diana, FLUENT Product Market Manager, Fluent Inc. 

Contours of the surface dipole strength are shown on the top and bottom surfaces of a blunt flat 

plate, as predicted by the flow-induced noise model in FLUENT 

A comparison of FLUENT MHD predictions with measurements of normalized steel velocity as a 

function of imposed magnetic field at the meniscus of a steel mold. In the simulation, the 

meniscus velocity changes its direction slowly with increasing field strength, whereas in the 

experiment, the meniscus velocity changes its direction more rapidly. The sudden change in the 

actual casting process is due to the effects of injected argon gas, and these effects were not 

included in the simulation. 

Three UDF-based add-on modules have been developed 

for use with FLUENT 6.0. All three modules handle complicated 

geometry efficiently using unstructured grids, 

and are accessible through the graphical user interface. The 

modules have been subjected to the same level of testing 

as FLUENT 6.0, and full documentation and technical support 

are available. 

flow-induced noise prediction 

The noise generated by flows across the surface of an 

obstruction can be computed using the noise prediction module. 

This capability can be applied to the simulation of flowinduced 

noise in many industries. Some examples include 

noise generated by air flowing past the exterior mirror of a 

moving automobile and noise generated by the flow over 

landing gear attached to an airframe. Based on a transient 

turbulent flow simulation, the time variation of the acoustic 

pressure together with the sound pressure level (SPL) are 

calculated using Lighthill’s Acoustic Analogy. The large eddy 

simulation (LES) turbulence model is highly recommended 

for this purpose, since it can capture the wide band sound 

spectrum. The model predicts the power spectrum and surface 

dipole strength distribution. Results for flow across a 

flat plate are in good agreement with experiment data. 

magnetohydrodynamic modeling 

The interaction between an applied electromagnetic field 

and an electrically conductive fluid can be analyzed using 

the magnetohydrodynamics (MHD) module. This capability 

can be applied to the continuous casting of steel or aluminum, 

for example. The model, an upgrade of the MHD 

model in FLUENT 4, simulates the flow under the influence 

of either constant or oscillating electromagnetic fields. A prescribed 

magnetic field can be generated by selecting simple 

built-in functions or by importing a user-supplied data 

file. Coupling between the flow and the magnetic field is 

modeled through the induced current (due to the movement 

of conducting material in the magnetic field), and the 

effect of the Lorentz (J x B) force as a source term in the 

momentum equations. The capability is compatible with both 

the discrete phase and volume of fluid models. The effect 

of the discrete phase on the electrical conductivity of the 

mixture can also be included. 

continuous fiber modeling 

In the fiber spinning process, molten polymer is extruded 

through a spinneret, which normally contains hundreds 

of holes, to form multiple fibers. The fibers are then solidified 

and drawn down in a quenching chamber. The final 

fiber strength and quality is strongly influenced by the gas 

flow field surrounding the fibers, including the rate of convective 

cooling or heating and the concentration of the 

gases within the quenching chamber. The fiber module in 

FLUENT 6.0 is an upgrade to the model that originally appeared 

in FLUENT 4. It includes the effect of numerous fibers with 

complete coupling between the fibers and gas flow. Gravity 

effects, friction with the surrounding gas, as well as heat and 

mass transfer are included. The model predicts the effect 

of fiber motion on the flow field as well as the fiber temperatures 

in the quench box. ■ 


product news 

Fluent’s Ted Blacker Wins the 

Meshing Maestro Prize 

by Dipankar Choudhury, Chief Technology Officer, Fluent Inc. 

The Tenth International Meshing Roundtable conference 

was held last fall in Newport Beach, CA. 

One of the highlights of this annual meeting is the 

naming of the Meshing Maestro, a coveted award that 

is given to a conference poster presenter who has generated 

a mesh that exhibits innovative technology, and 

is both eye-catching and technically sound. Ted 

Blacker, the project leader for GAMBIT, was last year’s 

winner of this prestigious award. His poster also won 

the “Best Technical Poster” award. 

The clown grid, one of the examples submitted by 

Blacker, made use of technology that was developed 

at Fluent Inc. by Blacker and his colleagues Richard Smith, 

Yongheng Shao, and Jin Zhu. In particular, new advances 

in mesh density controls were used that are now available 

in GAMBIT. These controls, called size functions, 

are aimed at eliminating automation obstacles during 

meshing, particularly when generating a tetrahedral mesh. 

Historically, most volume meshing problems are related 

to a bad surface mesh. The problematic surface mesh 

typically doesn’t capture the geometry well, or isn’t sized 

appropriately for thin regions of the geometry. It is also 

particularly important in CFD analysis that the gradation 

of the mesh be tightly controlled. This control limits 

transition rates from small to large elements, allowing 

capture of the boundary layer phenomena as well as 

control over solution accuracy. 

Although density control is not new in the meshing 

community, this technique is unique in how grading 

controls radiate or propagate to surrounding regions 

in a tightly controlled manner. For example, the eyebrows 

on the clown have a tight curvature, which is captured 

through a curvature-based size function. Not only 

is the eyebrow adjusted, however, but portions of the 

geometry in close proximity are included in the sizing 

effects as well. The forehead near the eyebrow attachment 

and even the interior of the eyelid show a graceful, 

controlled gradation of size. This ensures that the 

volumetric tet mesher can successfully fill this region with 

well-shaped elements, with minimal intervention by the 

user. A simple size function was defined to capture the 

curvature and set the gradation rate. This size function 

was attached to the volume and the meshing initiated. 

The software then generated the needed octree background 

grid and automatically guided the meshing based 

on these controls. (An octree is a hierarchical structure 

used in certain grid generation algorithms. It begins with 

Ted Blacker and his 

winning surface mesh 

a coarse background grid that is recursively divided until the 

desired grid density is achieved.) 

The technical advance that is central to the new controls 

in GAMBIT is accomplished by imposing individual size 

functions (such as the curvature of individual surfaces) on 

the underlying octree-based background grid. The octree 

depth (the number of levels in the hierarchy, which corresponds 

to the grid density) adjusts automatically to capture 

regions of importance in the size function. With the aid of 

the octree background grid, the size functions can then radiate 

beyond the regions where they are defined to accomplish 

the control and effects as desired. Three types of size 

functions are available, and these can be specified individually 

by the user. The edge, face and volume meshing tools 

then obtain sizing information directly from the background 

in a highly efficient manner. ■ 


computing 

FLUENT Users 

Capitalize on 

Parallel 

Processing 

by Liz Marshall, Fluent Inc. 

The computing potential available to 

today’s CFD engineers is nothing short of 

remarkable. Ten years ago, only the most 

adventurous CFD practitioners used models with 

more than 100,000 cells. Many simulations of 

this size could only be solved on the supercomputers 

of the day. Since then, scientists and 

engineers have scaled up to larger and larger 

problems, fueled by ever-faster hardware at steadily 

decreasing cost. The drop in price of processors 

and memory has coincided with advances 

in software technology to make parallel computing 

within the reach of many companies. 

For large scale problems, parallel processing algorithms 

have been introduced that allow a calculation 

to be segmented into two or more 

partitions that are solved simultaneously on different 

CPUs. Multi-processor workstations, and 

networks of single or multi-processor machines 

are now routinely being deployed at companies 

around the world to make faster work of 

simulations of all kinds using parallel processing. 

Fluent software users are among those who 

have taken advantage of this trend, thanks in 

part to the robust and scaleable parallel processing 

capabilities of the software. 

variety of hardware 

There are many ways that a parallel calculation 

can be performed. Multi-processor machines 

contain two or more CPUs, and can be based 

on RISC (running UNIX) or Intel (running 

Windows or Linux) architecture. On a dualprocessor 

machine, for example, the two processors 

share the memory in the system. The shared 

memory enables independent processes to communicate, 

using a technique called shared memory 

processing (SMP 1 ). Single, or serial 

processor machines, which contain only a single 

CPU, can be connected over a network to 

form a cluster. When a network of such machines 

performs a calculation in parallel, the process 

is called distributed memory processing (DMP). 

Unlike shared memory processing, where a single 

machine manages all the memory, with 

distributed memory processing the memory 

is managed locally on each machine; here, communication 

among processes occurs over a network 

rather than through shared memory. 

Multi-processor machines can also be networked 

to other multiple or single processor machines. 

Calculations run on a cluster of this type can 

use a process called distributed shared memory 

processing (DSMP, or often just DSM). 

1 

SMP traditionally stands for Symmetric Multi- 

Processor, used as a designation for a system that 

supports shared-memory parallel processing. 

Contours of cell partition on a car surface for a mesh subdivided into eight partitions 


computing 

“ In addition to its superior accuracy, ease of use and 

consistency, FLUENT is also absolutely amazing in its 

parallel processing ability. We assembled a small 

Linux cluster and obtained a parallel processing 

license. FLUENT performed flawlessly in our clustered 

environment the first time we tried it. Setting up 

and running a job in parallel is seamless to the end 

user, making FLUENT the ultimate return on 

investment in simulation tools.” 

– Ryan Huizenga 

CAD Systems Supervisor 

Litens Automotive Group 

FLUENT users have employed all of the above approaches 

for large jobs in need of parallel processing. Rodney 

Balzar from Briggs & Stratton Corporation uses a twoprocessor 

HP J6000, with 1024 MB of RAM shared by 

each processor. His simulations of turbulent flow with 

heat transfer typically involve more than three million 

cells. Jim DeSpirito at the US Army Research Laboratory 

(ARL) has a large computing facility at his disposal. The 

Major Shared Resource Center at ARL has over 1200 processors 

on SGI Origin 2000, Origin 3800, and IBM SP supercomputers, 

most of which have hundreds of gigabytes 

of RAM. DeSpirito’s group is one of many that use the 

facility, but he rarely has to wait long in the queue to 

launch jobs. He finds that he gets the best performance 

if he sets a limit of about 200,000 cells on each CPU. 

Thus, jobs involving five million cells typically use 28 to 

32 processors, while those involving 16 million cells work 

well with 64-96 processors. At Hamilton Sundstrand, Gary 

Post uses a cluster of six dual-processor Dec Alphas, running 

UNIX, each of which has 4 GB of memory. The 

machines are networked to each other, but are segregated 

from the rest of the corporate network. His typical 

runs, which include combustion and radiative heat 

transfer, involve from 500,000 to one million cells, are 

usually done using six processors on three machines. He 

often needs to find six available processors on more than 

three machines, and is grateful for the flexibility that allows 

him to choose either one or two from each machine. 

Giri Manampathy at GE Aircraft Engines usually uses a 

cluster of dual-processor HP workstations. The machines 

are linked via a high-speed network, and are segregated 

from all of the other computers on the company network. 

For problems using up to six million cells, most 

of which involve turbulent combustion, he typically makes 

use of 12 CPUs on this network. When not using the 

HP cluster, he can also elect to use an 8-processor shared 

memory PC. 

The PC, with Intel-based architecture, has gained popularity 

among Fluent software users, and indeed, among 

engineers and scientists running computationally intensive 

simulations of all types. For FLUENT users, parallel 

computing is available for both the Windows and Linux 

operating systems and on CPUs from both Intel and 

Advanced Micro Devices (AMD). At Babcock Borsig, Ken 

Hules uses a cluster of one- and two-processor machines 

using Intel and AMD hardware running Windows. With 

twelve CPUs at his disposal on a high speed network 

that is segregated from the corporate network, he usually 

runs FLUENT jobs on four to six processors at a time, 

using load balancing (through FLUENT’s partitioning tools) 

to effectively mix the range of CPU speeds in use. His 

problems are large, in excess of two million cells, but 

are primarily characterized by complex physics, including 

coal combustion and water sprays. Paul Chapman 

at Alstom Power also uses a collection of UNIX and PC 

workstations, but has added a Linux-based cluster for 

larger cases. The cluster has six dual-processor machines, 

with direct high-speed connections between each of the 

nodes. It is ideal for larger cases which can exceed two 

million cells, including radiation and chemical reactions 

associated with simulations of large scale power and process 

equipment. Considering the total cost of running large 

CFD simulations, the economics favor running on the 

fastest possible hardware. For this reason, they have upgraded 

the hardware twice in the past two years, with the 

latest swap to AMD processors running Linux. 

performance enhancements 

All of the FLUENT users interviewed have found impressive 

gains in their computing ability since switching to 

parallel processing. For Manampathy at GEAE, who has 

been using parallel processing for about a year, performance 

has scaled linearly as he has added compute nodes during 

this time. Grid independence is very important to 

him, so with parallel processing, he can always ensure 

that each solution satisfies this requirement. Balzar at 

Briggs & Stratton has seen a four-fold improvement after 

adding a second node. This exaggerated improvement 

is most likely due to the fact that his calculations were 

too large to fit inside the available RAM on his serial machine. 

continues on page 41 • 


computing 

Linux Clusters: 

Inexpensive Power for 

High-End CFD Computations 

by Jonas Larsson, Volvo Aero Corporation, Trollhättan, Sweden 

“We are extremely satisfied with FLUENT’s stability and 

performance on our new 150 CPU Linux cluster. Over the 

three years Volvo Aero has been using Linux clusters, Fluent 

has consistently met and exceeded all our expectations. By 

switching to running FLUENT on Linux clusters, we have been 

able to increase our computational resources by a factor of 10.” 

– Peter Emvin, Ph.D. 

Manager, Aero and Thermodynamics, Volvo Aero Corporation 

Jonas Larsson in front of the 150 CPU Linux cluster 

A multi-stage axial compressor simulation 


An air-intake 

simulation 

of a Swedish 

fighter jet 

By switching to Linux clusters, the CFD 

group at Volvo Aero Corporation has been 

able to increase their computational 

resources by a factor of ten with a reduced hardware 

budget. The transition from expensive 

parallel UNIX machines to large Linux clusters 

has been a tremendous success, and has led 

to huge improvements both in quality and leadtime 

for all CFD work done. 

The CFD group at Volvo Aero were pioneers 

in using Linux clusters. They bought their first 

Linux cluster three years ago, and today have 

more than 150 CPUs in the cluster, which is 

used only for CFD simulations using FLUENT 

and their in-house CFD code, VolSol. The CFD 

engineers are very happy with the new computing 

environment. Stability and performance 

with FLUENT and VolSol have been markedly 

better than on their old UNIX servers. Because 

the engineers were already familiar with the 

UNIX environment, the migration to Linux has 

gone smoothly. UNIX desktop machines are 

still used for most pre- and post-processing work. 

Volvo Aero Corporation designs and manufactures 

components for military jet engines, 

commercial jet engines, and rocket engines. 

CFD plays an important role in all of these areas 

and has traditionally been a very strong discipline 

at Volvo Aero. Most of the work is performed 

at the CFD Center of Excellence, a leading 

engineering department that has a long history 

of CFD experience, and which serves all 

business units of Volvo Aero. Today there are 

twenty-four engineers; one adjunct professor, 

twelve PhDs, and eleven MScs. The cluster is 

used only by this group and has made it possible 

for them to run a whole new class of problems. 

Transient, multi-stage turbomachinery 

simulations with several million cells are now 

easily and routinely run using parallel processing 

on the cluster. 

When the cluster was first assembled the 

philosophy was to use as many standard, offthe-shelf 

components as possible. The compute 

nodes are normal desktop PCs and the 

network is normal 100Mbs, switched Ethernet. 

A faster network or non-standard nodes can 

easily double the costs. Using standard components 

also makes it much easier to maintain 

and upgrade the cluster, since most 

companies already have a well-established channel 

for buying and maintaining their desktop 

PCs. New nodes can easily be added as the 

need arises and old slow nodes can be removed 

and re-used as desktop office PCs. 

The switch to Linux clusters has also eliminated 

the need for a queue system. The only 

type of scheduling used now is a script that displays 

the cluster load on a web page. This allows 

users to select available CPUs on an as-needed 

basis. With today’s low cost per CPU, it makes 

more sense to buy new nodes as the need arises, 

rather than force users to wait for CPU in a 

queue system. 

With more than three years of experience 

running CFD on large Linux clusters, Volvo Aero 

Corporation has no doubt that this is the computing 

platform of the future. Volvo Aero has 

also started to replace their desktop UNIX machines 

with Linux machines – creating a homogenous, 

low-cost/high-performance computing environment 

that can scale to any future needs. ■

computing 

The Impact of the 

Web on the Engineering 

Simulation Process 

By Paul Bemis, Vice President, eBusiness, Fluent Inc. 

The internet and web technologies are 

continuing to revolutionize the ways 

in which people and organizations communicate. 

The engineering community is 

now poised to take advantage of this electronic 

infrastructure to increase efficiencies 

in product development processes. 

Moreover, with even greater improvements 

on the horizon as a result of higher bandwidth 

networks and high performance personal 

computers, the potential impact on 

the design and development process is significant. 

For engineering applications, 

these capabilities will provide the ability 

to simulate more complex systems faster 

and more efficiently than ever before, using 

“pay as you go” software on thin clients 

that access remote “compute servers” via 

the LAN, a WAN (local- and wide-area networks, 

respectively), or over the internet. 

Thin client systems use centralized servers 

that provide application software to users 

on a network, in contrast to fat client systems, 

where every desktop has a PC or workstation 

outfitted with individual installations 

of the software. An increasingly popular 

method for deployment of this method uses 

a Remote Simulation Facility (RSF) that specializes 

in providing this service to users 

through the internet. 

Using the web as a delivery mechanism 

for engineering solutions has significant benefits 

for many end users. Companies can 

run simulations affordably, scaling the cost 

of performing simulations to demand, without 

the conventional investment required 

for software and hardware. A Remote 

Simulation Facility arrangement readily 

accommodates the rise and fall of computational 

power needs and solution time 

required for peak periods and lulls 

between jobs. Another benefit of this model 

is the potential to increase the rate at which 

users gain access to new software versions. 

It is not unusual for users to wait nearly 

one year for new versions of application 

software to reach their desktop systems. 

Using the RSF model, new versions can be 

deployed more quickly without adversely 

affecting the desktop user. Further, older 

versions can be kept available for users who 

have not yet migrated to the newer version. 

Administration and support of the applications 

is more efficient and less disruptive, 

due to the central nature of this model. 

One of the more far-reaching benefits 

of a web-based RSF is that it can facilitate 

greater collaboration between users. This 

is primarily manifested through the centralized 

nature of the web infrastructure. 

Specifically, a file located on a web server 

appears to all users, regardless of their 

physical location, as the same file. This means 

that users will be able to interact across 

geographical and organizational boundaries 

within one company, or across the 

entire supply chain. An example of this type 

of collaboration might be between an automotive 

tier-one supplier and one of the big 

three automotive companies. The supplier 

will use the RSF as a mechanism to run 

the exact simulation sequence using 

methods and tools specified by the buyer. 

Once the simulation is complete, the results 

and reports become available to the buyer 

as files on the remote facility, eliminating 

the need to move data from one location 

to another. Thus, the RSF becomes the central 

point for collaboration, the repository 

for shared files, and serves to implement 

best practices throughout the simulation. 

Another potential area of collaboration 

is the Simulation Portal. Recent developments 

in web technologies now allow a 

web portal to become a location for groups 

of users to develop a cyber-community. 

For example, users interested in one particular 

type of simulation could create a 

small group that could exchange ideas via 

email about methods and solutions used 

to solve specific problems. Using the same 

portal, users could also create custom templates 

for solving application specific 

problem types that could be shared and 

distributed using access control methods. 

When used in combination, a web-based 

Remote Simulation Facility integrated 

within a custom Simulation Portal opens 

up engineering simulation and collaboration 

to a much larger audience of users than 

ever before. The implementation of this 

solution is generally referred to as an 

Application Service Provider model. At first 

blush, many seasoned users often dismiss 

the ASP model as a throw back to the times 

of large shared mainframes and high costs. 

However, with today’s reduced computing 

costs, and the higher bandwidth promise 

of the next generation internet, a fresh 

look at the RSF model is well worth the 

time. The challenge for us all is in developing 

streamlined engineering processes 

and innovative business strategies that take 

best advantage of the new tools for this 

growing body of potential users. ■ 


support corner 

More and more FLUENT users are taking advantage of parallel processing 

to reduce turnaround time and fully utilize available hardware. 

Based on the client interviews in the related article on page 

36, it is clear that parallel processing is used today across a wide range of 

applications and industries. In this article, some parallel processing basics 

are presented along with representative performance statistics and an update 

on new parallel processing features in FLUENT 6.0. If you haven’t tried running 

in parallel yet, this information should convince you to give it a try 

and help get you started. 

Getting Started 

with Parallel 

Processing 

by Kirk L. Oseid, US Director of Support, Fluent Inc. 

hardware requirements 

Many hardware systems today support parallel processing, as shown in 

the list below. On these systems, a FLUENT calculation can be shared by 

two or more processors. 

• Multi-processor UNIX (including Linux) machines 

• Multi-processor Windows-based machines 

• Networks of UNIX workstations 

• Networks of Windows-based workstations 

Since many engineers have access to one or more of these systems, 

the option for parallel processing is now widely available throughout the 

FLUENT user base. 

how parallel processing speeds up the calculation 

In an ideal world, the time required to run a calculation on two processors 

should be half that required to run it on a single processor. Associated 

with this reduction in calculation time, however, is the addition of time required 

to continually communicate information between the processors as the calculation 

proceeds. This computational overhead contributes to the performance 

rating given to a multi-processor calculation. Each time the number of processors 

doubles, the computation time on each processor halves, but the overhead 

continues to increase. 

The ideal performance improvement for a parallel calculation is one where 

the performance rating increases linearly with the addition of processors. 

After several years of dedicated effort, FLUENT is now impressively close to 

this ideal for most practical configurations. The graph at left shows the actual 

scale-up of the performance rating for several representative hardware 

systems. The medium-sized benchmark problem used for this set of tests 

is that of a turbulent flow in a domain of approximately 250,000 cells. 

This and other benchmarks are described in detail on the Fluent User 

Services Center (www.fluentusers.com) and on the corporate web site 

(www.fluent.com/software/fluent/fl5bench). 

getting started 

You can run FLUENT in parallel if your current license allows for two 

or more FLUENT processes, and if you have two or more CPUs exclusively 

available to you. By following the steps outlined below, you can be up 

and running quickly. 

Performance ratings for a number of representative UNIX and Intel-based systems 

show linear or nearly-linear behavior 

1. Launch the parallel solver 

The parallel version of FLUENT can be launched on various platforms 

using commands like those shown in the table below. 

Platform 

FLUENT Launch Command 

Multi-processor UNIX Machine fluent 3d -t2 

Multi-processor Windows-based Machine fluent 3d -t2 

Network of UNIX Workstations 

fluent 3d -t -pnet -cnf=hostfile 

Network of Windows-based Workstations fluent 3d -t -pnet -cnf=hostfile 


computing 

In these examples the 3D version of the code is specified 

(3d), and two processes are started on the multi-processor 

machines (-t2). For the network examples, a process 

will be launched on each machine listed in the hostfile, up 

to a number specified by the -t flag. For the Windowsbased 

network example, it is assumed that the RSHD communicator 

software (included with the FLUENT distribution) 

has been installed. For instructions on building the hostfile 

file, please refer to Chapter 28 of the FLUENT User’s Guide. 

2. Read the grid (or case) file and partition 

Partitioning is the task of segmenting your computational 

domain and assigning the segments to individual processors. 

FLUENT will automatically partition the grid or case 

file for you, using defaults that should be close to optimal. 

If the grid or case has been partitioned previously, the partitions 

are retained, but they can be reviewed and adjusted 

at any time. FLUENT provides partition quality 

reporting, as well as state-of-the-art partitioning tools. 

3. Initialize and solve 

Initialize and compute the solution as you would in a serial 

(single processor) run. The only difference you should 

see is faster turnaround! 

parallel enhancements in FLUENT 6.0 

Parallel processing has been available in Fluent products 

since the mid-1990s, with improvements highlighted in every 

major release. In FLUENT 6.0, this trend continues, with numerous 

enhancements featured in partitioning controls and flexibility. 

For example: 

• Unpartitioned grids can be imported and 

partitioned in the parallel solver; 

• Stationary non-conformal interfaces can be 

partitioned directly in the parallel solver; 

• Partitioning can be invoked automatically, 

following grid adaption and remeshing; and 

• Two types of partitioners (Geometric and Metis, 

developed at the University of Minnesota) are 

now available for use. 

Dynamic load balancing (automatic cell migration between 

partitions) has also been added to help keep your FLUENT session 

running optimally. Changes due to local mesh adaption, 

new loads added to individual processors, and variations in 

network performance for clusters are now managed efficiently 

using behind-the-scenes technology. 

want to learn more? 

Check out the User Services Center to read more about 

parallel processing with FLUENT. You can also refer to the User 

Documentation CD, where a Parallel Processing Tutorial is provided 

to take you through the process in a step-by-step fashion. 

Call your local Fluent office with any questions you may 

have about using this exciting option at your site. Watch out, 

though. As you cycle through simulations at a faster pace, you 

may soon find your workload increasing as your colleagues 

approach you with more and more problems to solve! ■ 

Parallel Processing 

continued from page 37 

When this occurs, portions of the calculation 

must continually be swapped 

out of RAM to the disk so that other 

portions can be moved into RAM for 

active computation. Swapping, audible 

by the sound produced when data 

is written to a hard drive (often a rattling 

sound coming from the computer), 

can easily slow a calculation down by 

a factor of two. By adding a second 

processor and more memory, his calculations 

now easily fit into the available 

RAM, so his savings have been 

effectively quadrupled. For Post at 

Hamilton Sundstrand, who has been 

parallel processing for about two years, 

larger simulations with a step change 

in detail are now possible. For a typical 

combustion problem he usually 

needed an overnight run to compute 

a cold flow solution. He would then 

have to wait until the following day 

before he could ignite the flame and 

compute the final solution. Now, the 

setup and cold flow can be done in a 

single day, so that the flame solution 

can be performed that night. Whereas 

on a single processor machine, it might 

have taken two and a half days to solve 

a combustion problem with 200,000 

cells, it now takes one full day to solve 

one with over 500,000 cells. According 

to DeSpirito at ARL, whose simulations 

can exceed 10 or even 15 million cells, 

“Our problems would not be solvable 

without parallel processing.” 

Clearly, obvious benefits are realized 

for CFD simulations that rely solely 

on the solution of transport 

equations (species mixing and reactions, 

Eulerian multiphase, transient flow, etc.). 

The benefits are less apparent when 

the simulation involves particle tracking 

and is performed on a cluster. 

According to Hules at Babcock Borsig, 

while he achieves linear scale-up 

Running FIDAP and 

POLYFLOW in Parallel 

FLUENT is not the only software 

from Fluent that takes advantage of 

parallel processing. Most of the capabilities 

of FIDAP and POLYFLOW run 

in parallel on multi-processor machines. 

most of the time, the scale-up is reduced 

when he simulates coal combustion. 

This is because the particle tracking routines 

currently run at parallel speeds 

on shared memory machines only. (A 

distributed memory particle tracking 

model is planned for FLUENT 6.1.) 

Despite the current limitations, he is 

still pleased with the speed-up he 

achieves when compared to his serial 

runs of the past. 

In addition to the benefits of 

faster processors and algorithms for running 

calculations in parallel, high performance 

graphics cards have added 

the ability to visualize the results of larger 

models. Where the PC was previously 

incapable of rendering the 

results of large 3D simulations, the falling 

cost of 3D graphics hardware has 

allowed users to easily manipulate and 

animate CFD data, making post-processing 

an enjoyable experience. 

Advances in linking parallel calculations 

to real-time desktop post-processing 

will allow CFD modeling to extend far 

beyond its traditional boundaries in the 

years to come. 

In today’s engineering landscape, 

there are increased demands for 

higher accuracy from CFD simulations, 

and these are coupled with demands 

for more rapid turnaround times. To 

meet these demands, parallel processing 

will continue to play an ever-expanding 

role. Having evolved from algorithms 

for shared memory workstations to those 

for distributed memory clusters connecting 

single and multi-processor 

machines, parallel processing technology 

will continue to grow. Computers will 

continue to stun us as well, with their 

increased power and reduced costs. 

With these advances, the day will soon 

come when problems with tens of millions 

of cells will become routine. ■ 

Many platforms are supported, and 

upcoming releases will continue to focus 

on improving the usability, performance 

and robustness of parallel processing. 

■ 


partnerships 

Cooperative Research on Fuel Cells 

Fluent CFO Peter Christie (seated, left) and NETL’s Larry 

Headley (seated, right) sign the CRADA on PEMFC modeling 

In November 2001, Fluent entered into a 

Cooperative Research and Development 

Agreement (CRADA) with the US Department 

of Energy’s National Energy Technology 

Laboratory (NETL). This collaboration will focus 

on the development and validation of a 

FLUENT-based Polymer Eletrolyte Membrane 

Fuel Cell (PEMFC) model. 

NETL and Fluent have agreed to work together 

on the development of the PEMFC model, 

which builds on the existing capabilities of the 

FLUENT code to calculate fluid flow, heat and 

mass transfer, and chemical reactions. NETL 

will provide expertise in PEM fuel cell technology 

to assist in the implementation of submodels 

describing complex PEMFC physics. NETL will 

also work with Fluent in model validation by 

providing data from NETL PEMFC experiments 

to compare with model predictions and ensure 

model accuracy. The validated PEMFC code 

will then be made available to the public as 

part of the commercial Fluent software family. 

NETL will use the resulting model for their 

in-house studies of PEMFC systems. ■ 

“The result of this CRADA between NETL 

and Fluent Inc. will be a fully-validated, commercial 

CFD model of the PEMFC, including 

electrochemistry, electric field, and 

multiphase flow of water. This FLUENT-based 

tool will allow PEMFC designers and manufacturers 

to understand the detailed 

operation of their PEMFC cell and stack, which 

is critical information for design optimization.” 

– Dipankar Choudhury 

Chief Technology Officer, Fluent Inc. 

FLUENT prediction of water vapor mole fraction at 

the anode of a PEM Fuel Cell 

Parameterized Model Building for Climate Control 

Fluent and ICEM CFD Engineering 

have partnered to provide an 

up-front design tool for performing 

passenger comfort studies 

in automobiles. This easy-to-use application-specific 

tool, CABIN MOD- 

ELER, developed by ICEM CFD 

Engineering, allows FLUENT users to 

define and mesh the interior geometry 

of a sedan, mini-van, or hatchback 

(SUV) by simply defining 

dimensions on a parameterized 

template. 

Through its parameterized 

approach, CABIN MODELER provides 

a quick and easy way to perform 

climate control studies in the 

absence of a CAD file. Parametric 

design studies varying not only compartment 

size, shape and angle, but 

also register locations, seat clearances 

and instrument panel details can be 

quickly performed throughout the 

design process to ensure optimized 

ventilation systems and identify 

potential design flaws long before 

prototypes are built or even detailed 

CAD data exists. 

CABIN MODELER creates a 

tetrahedral volume mesh, with 

optional prism layers on wall surfaces. 

Meshing is fully automatic, with default 

meshing parameters tuned to the 

geometry, but with the ability of the 

user to override defaults and exert 

control. The mesh is saved in native 

FLUENT format and can be read directly 

into the FLUENT solver. 

Beyond this, CABIN MODELER 

can be a valuable tool in the later stages 

of vehicle compartment design. As 

detailed CAD data becomes available, 

it can be used to facilitate the cleanup 

and meshing process by merging 

the actual CAD geometry (e.g. 

the dashboard) into the parameterized 

template, thus streamlining the 

process from CAD to analysis. ■ 

more.info@ 

cabinmodeler@fluent.com 

CABIN MODELER provides parameterized model building and automated 

meshing for FLUENT automotive climate control simulations 


partnerships 

Aerosol/Hydrosol 

Modeling in FLUENT 

Fluent partners at Chimera Technologies have developed a new 

“plug-in” model for FLUENT that addresses aerosol and hydrosol 

behavior. Referred to as the Fine Particle Model (FPM), the new 

model simulates the formation, growth, transport, and deposition of 

particles in systems influenced by fluid flow, heat transfer, and chemical 

reaction. Applications include chemical reactors, materials processing, 

pollutant formation and transport, nano-particle sprays, particle inhalation 

and transport, and other systems involving sub-micron particles 

in gas or liquid systems. 

In contrast to Fluent’s Lagrangian Discrete-Phase Model (DPM), the 

FPM treats particles in an Eulerian reference frame, and allows particle-particle 

interactions. It describes the spatial and temporal evolution 

of the particle size distribution accounting for nucleation and growth 

of particles, including effects like Brownian motion, adsorption, condensation, 

and coagulation. The FPM is a set of User-Defined Functions 

(UDFs) that work with FLUENT 6. It includes a native FLUENT GUI interface 

and also allows users to modify and extend the model microphysics 

and chemistry via their own UDFs. 

Release of the FPM is planned for late-2002. ■ 

Flowmaster Group 

Announces FLUENTLink 

Flowmaster Group has developed an interface between FLUENT 

and FLOWMASTER ® , a leading 1D fluid flow simulation code. 

FLUENT users will be able to perform co-simulation analysis with 

the two codes, gaining the benefit of low memory use and high speed 

1D simulation coupled to detailed 3D analysis from FLUENT. 

Typical applications include automotive thermal modeling, in which 

a 1D FLOWMASTER simulation can be used to accurately determine 

flow rates, pressures and temperatures for the external circuit including 

components such as the water pump, hoses, radiator, and thermostat. 

This simulation might be coupled to a highly detailed 3D FLUENT 

simulation of the flow distribution in the engine cylinder block and 

heads. 

The initial release of FLUENTLink will be available for FLUENT on 

HP workstations under HP/UX and FLOWMASTER on PCs running Windows ® 

across a network. Later releases, due before the end of the Q2, will 

add further platform support for FLUENT. ■ 

The Fine Particle Model describes the nucleation and growth of particles in 

aerosol and hydrosol systems, including the influence of chemical reactions, fluid 

flow, and heat transfer. 

more.info@ 

www.aerosols.com 

partnerships@fluent.com 

more.info@ 

www.flowmaster.com 

or contact Flowmaster Group 

at +44 1327 306000 

Turn-key Parallel Computing Solutions 

Fluent has teamed with Soho Corporation, an HP 

Technical Computing Channel Partner, to offer customers 

in North America a fully integrated and tested Linux cluster 

computer system for cost-effective parallel computing 

with FLUENT. Since its inception in 1996, Soho has 

been dedicated to the support and implementation of 

infrastructure requirements for technical computing applications. 

Recently, Soho has worked with Fluent to fully 

understand the unique requirements of running FLUENT 

on a Linux cluster. They assemble the cluster at their facility, 

and fully configure it for running FLUENT, in a process 

that includes installation of the operating system, cluster-enhancing 

applications, drivers, and utilities, and validation 

using FLUENT benchmarks. The complete 

system is then installed at the customer’s site, where the 

FLUENT benchmarks are run a second time. Following 

the installation, Soho serves as the single point of contact 

for cluster infrastructure support management. ■ 

more.info@ 

fluent@sohocorporation.com 


around Fluent 

Fluent Attends Launch of 

Ferrari Formula 1 Race Car 

Fluent Worldwide 

Fluent Inc. 

10 Cavendish Court 

Lebanon, NH 03766, USA 

Tel: 603 643 2600 

email: info@fluent.com 

US regional offices 

Evanston, IL 60201 

Tel: 847 491 0200 

Ann Arbor, MI 48104 

Tel: 734 213 6821 

Santa Clara, CA 95051 

Tel: 408 522 8726 

Morgantown, WV 26505 

Tel: 304 598 3770 

Fluent Europe Ltd. 

Sheffield Airport Business Park 

Europa Link 

Sheffield, S9 1XU, England 

Tel: 44 114 281 8888 

email: info@fluent.co.uk 

The Maranello, Italy-based Ferrari Formula 1 Team 

recently unveiled their eagerly awaited 2002 race 

car to a crowd of 1000 specially invited guests, 

including 500 motor racing journalists from the print 

and televised media around the world. Ferrari’s new 

car launch was a predictably glitzy show, to match 

the confidence of the World Champions for the last 

two years. Gerard De Neuville, Fluent’s Vice President 

and Manager of Fluent France, was invited to the launch 

as a Technical Partner of the Ferrari Formula 1 Team. 

academic news 

Italian University Researcher 

Wins Prestigious Award 

Dr. Francesco Migliavacca 


He was accompanied by Martine De Neuville, 

Fluent’s Communications Director for South Europe, 

and Marco Rossi, the Manager of Fluent Italia. In the 

picture they are seen beside Luca di Montezemelo, 

President of Ferrari, and the all-new 2002 race car that 

will be driven again this year by Michael Schumacher, 

the world champion driver from Germany. After working 

closely with Scuderia Ferrari for several years, it 

was a proud moment, and another motor racing first, 

for Fluent. ■ 

November 16, 2001 was an important day for LaBS, the Laboratory 

of Biological Structure Mechanics of Politecnico di Milano in 

Milan, Italy. Dr. Francesco Migliavacca, a member of the LaBS 

staff, was awarded Le Scienze Medal for his outstanding accomplishments 

in the study of the haemodynamics after paediatric cardiac surgery. 

His work consists of mathematical modeling using FIDAP and FLUENT 

software. At the same time Dr. Migliavacca was also awarded a Medal 

from Mr. Ciampi, President of the Italian Republic. 

Le Scienze is the Italian edition of the journal Scientific American. 

Le Scienze Medal was established in 2000 and is presented to three young 

researchers, working in Italy, whose results have been internationally 

acknowledged, in recognition of distinguished contributions to the different 

fields of science. This year the conferral ceremony took place at 

the Università degli Studi, Milan, and the Awards were presented to 

Dr. Migliavacca (for engineering), Dr. Roberto Bini (for chemistry) and 

Dr. Elena Cattaneo (for medicine) by Prof. Enrico Decleva and Prof. Paolo 

Mantegazza, Rector and Rector Emeritus of the Università degli Studi, 

respectively. ■ 

European regional offices 

Fluent Benelux 

Wavre, Belgium 

Tel: 32 1045 2861 

Fluent Deutschland GmbH 

Darmstadt, Germany 

Tel: 49 6151 36440 

Fluent France SA 

Montigny le Bretonneux, France 

Tel: 33 1 3060 9897 

Fluent Italia 

Milano, Italy 

Tel: 39 02 8901 3378 

Fluent Sweden AB 

Göteborg, Sweden 

Tel: 46 31 771 8780 

Fluent Asia Pacific Co., Ltd. 

Shinjuku Center Building 50F 

1-25-1, Nishishinjuku, Shinjuku-ku 

Tokyo 163-0650, Japan 

Tel: 81 3 5324 7301 

Osaka, Japan 

Tel: 81 6 6445 5690 

Fluent India 

Pune, India 

Tel: 91 20 6119424 

distributors 

ATES - Korea 

Beijing Hi-key Technology Corporation 

Ltd. - China 

Figes Ltd. - Turkey 

Fluid Codes Ltd. - U.K. (serving the Middle East) 

Hungarian Combustion Ltd. - Hungary 

J-ROM Ltd. - Israel 

LEAP Australia Pty., Ltd. 

Australia & New Zealand 

Process Flow - Finland 

RCCM - Japan (FIDAP & POLYFLOW only) 

Scientific Formosa, Inc. - Taiwan 

(not an Icepak distributor) 

SimTec Ltd. - Greece 

SMARTtech Services and Systems, Ltd. 

Brazil 

SymKom - Poland 

Taiwan Auto-Design Company (TADC) 

Taiwan (Icepak only) 

Techsoft Engineering s.r.o 

Czech Republic 

Thermal Technologies - South Africa 

more.info@ 

Visit www.fluent.com 

for specific contact information

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