Wind Turbine - Vitajte na WWW stránke užívateľov ANSYS-u · FLUENT Users Capitalize on Parallel Processing Linux Clusters: Inexpensive Power for High-End ... Sharp Labs Uses FIDAPto

AutomotiveFor the Driver Who Has Everything

HVACImproving the Air for Arias

Power GenerationThe Power of SOFC Fuel Cells

SportsThe Winning Edge

Materials ProcessingSupplement Inside!

In theWake

of a

WindTurbine

VOL XI ISSUE I • SPRING 2002

A P P L I E D C O M P U T A T I O N A L F L U I D D Y N A M I C S

Contents

30

S216

applications

9 environmentalUK Water Seminar on Tap

10 chemicalImproving Sparger PerformanceStatic Mixers by Design

13 aerospaceFatal Concorde Fire ExplainedUnsteady Flow Behind a High

Speed Train

16 sportsThe Winning Edge

19 appliancesFrost-Free ChillingThermal Mapping of a Hermetic

Compressor

feature stories

5 wind energyIn the Wake of a Wind TurbineWind Turbine Blade AerodynamicsMapping a Wind Farm

36 computingFLUENT Users Capitalize on Parallel ProcessingLinux Clusters: Inexpensive Power for High-End

CFD ComputationsThe Impact of the Web on the Engineering

Simulation Process

21 electronics coolingThermal Modeling of a Multi-Unit Charger

for Li-ion Batteries

23 hvacSmoke Management at Frankfurt AirportImproving the Air for Arias

28 automotiveCustomized Phosphate Dip Tanks for CarsArrows Formula 1 Team Moving Up the GridFor the Driver Who Has Everything

32 power generationThe Power of SOFC Fuel CellsFlameless Burner Validation

20

38

14

42

S3

departments

34 product newsNew Specialty Modules for FLUENT 6.0Fluent’s Ted Blacker Wins the Meshing Maestro Prize

36 partnershipsCooperative Research on Fuel CellsParameterized Model Building for Climate ControlAerosol/Hydrosol Modeling in FLUENTFlowmaster Group Announces FLUENTLinkTurn-key Parallel Computing Solutions

40 support cornerGetting Started with Parallel Processing

44 academic newsItalian University Researcher Wins Prestigious Award

44 around fluentFluent Attends Launch of Ferrari Formula 1 Race Car

materials processing supplement

S2 business caseMeeting the Wide-Ranging CFD

Needs of Materials Processing

S3 glassReverse-Engineering a Gob

of GlassEnsuring Successful Delivery of

Molten Glass with CFD

S4 plasticsDesign Calculator Takes the

Guesswork Out of HeadlightEngineering

Preventing Punctures in SterilePackaging

S6 semiconductorOptimizing Photo-Resist Film

UniformitySharp Labs Uses FIDAP to

Accelerate Promising Flat PanelDisplay Research

Optimization of Vapor Purgingin Wafer Isolation Pods

S8 metallurgySteel Industry Applications at

ARCELOR

Editor’s Note

FluentNews is published by

10 Cavendish CourtLebanon, 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 FluentIncorporated. All other products or

name brands are trademarks of theirrespective holders.

A long withthe steadygrowth of

our business duringthe past severalyears has been thesteady growth of our

corporate newsletter. Launched in April, 1986,Volume 1, Number 1 of the Fluent User’s Newsletterprovided an update on the development of newphysical models in FLUENT 2.9 (transient flow,pressure boundary conditions, and conjugate heattransfer, to name a few). It reported on the firstannual Users’ Group Meeting, and highlightedthe capabilities of a new product undergoing test-ing, FLUENT/BFC, our first to offer body-fittedcoordinates. A Frequently Asked Questions sec-tion focused on issues such as convergence andsetting turbulence boundary conditions. A two-page article on the solution of natural convec-tion problems using FLUENT was also featured.

Since then, the newsletter has tracked the steadyevolution of simulations performed with our soft-ware: from simple 2D case studies to complex,industrially relevant analyses providing return oninvestment for our customers. The name FluentInc. Newsletter was introduced in 1993, and withit, a full color format. Articles typically dealt withmodeling advances, validations performed in-house, product updates for solver and pre-pro-cessing software, and application stories by ourclients. The title Fluent News was adopted in theSpring 1997 issue, along with a new format thathighlighted a CFD image on the front cover. Duringthe next several years, Fluent News underwentoccasional upgrades as the number and depthof the application stories steadily increased.

With the current issue, we have once againundergone a design change to better accom-modate the increased number and quality of appli-cation stories submitted by you, our customers.Seasoned readers of Fluent News will notice thatseveral stories have expanded to two or threepages to allow room for more technical details;stories about the Frankfurt Airport and the UKSports Institute are examples. Sections with sto-ries on related topics have been added; wind ener-gy and computing are featured in this issue.Application stories continue to abound, with exam-ples ranging from air flow inside the BudapestOpera House to automotive paint spraying sys-tems. The supplement focuses on the breadthof applications found in the materials process-ing industry, with contributions from glass, semi-conductor, steel, and plastics manufacturers.

The changes we have implemented in thisissue of Fluent News are the result of our grad-ually changing focus over the past sixteen years– from a newsletter in which we tell you abouthow our software works, to a magazine in whichour customers tell each other about how our soft-ware works for them. We hope that you can ben-efit from the information contained in the pagesthat follow, and that you will let us know aboutyour own experiences with our software. Pleasecontact us at [email protected] with your comments, suggestions, and stories of your successes. ■

Best regards,

Liz Marshall, Editor

On the Cover:Line contours of velocitymagnitude behind a wind turbine

1986 1993 1997

Fluent NEWS spring 2002 5

wind energy

Many companies through-out the world have beenapplying their skills and

expertise to the development ofrenewable energy sources. Thenumber of companies involved in theproduction of clean and sustainableenergy will undoubtedly increase inthe near future due in part to a com-mitment to the Kyoto Protocol(1997), which calls for sweeping reduc-tions in man-made green-house gasemissions, and in part to an increasedawareness of the environment.

One of the most abundantsources of renewable energy iswind, and technology exists todayfor the efficient extraction of ener-gy from wind for power generation.The efficiency of wind power is tiedto a number of factors, one of whichis the positioning of wind turbinesnear other wind turbines or structures.

Decreased distances give rise to wakeeffects for the downstream units, whichcan lead to changeable wind loads,reduced energy yield, and vibrationinduced fatigue on the rotors andpotentially on nearby power lines.

One popular operation conceptfor wind turbines allows for adjust-ments in the blade pitch to delivera reasonably constant power outputwhen there are variations in the windspeed. The wake behind these so-called “pitch-regulated” wind turbinesdepends on a number of parame-ters, such as blade geometry, pitchangle, and rotor speed on the hard-ware side and wind velocity, turbu-lence characteristics, and windgradients on the environmentalside. The large number of govern-ing parameters makes it difficult tojudge whether wake influences willlead to loads not considered during

the original construction process. In a recent series of simulations atTÜV Nord e.V., FLUENT has been usedto examine the wakes behind windturbines of this type on the basis of their geometry and operating characteristics.

TÜV Nord e.V. is one of Germany’sTechnical Inspection Agencies andhas the goal of protecting human-ity, the environment, and propertyagainst detrimental effects caused bytechnical installations and systems ofevery kind. To this end, it promotesthe economic installation or manu-facture and use of technical equip-ment, production, and operatingfacilities.

In a typical simulation, approxi-mately 650 data points are used tocreate the geometry of a single rotorblade. A fine grid on the whole rotorsurface is used to create a volume

In the Wake of a

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

Velocity contours behind one turbine show thewake effect on a second, smaller turbine

6 Fluent NEWS spring 2002

mesh of about 750,000 cells that grad-ually coarsens as the distance fromthe blades increases. The dimensionsof the flow domain are adjusted tosuit the needs of the specific prob-lem. Downstream distances of six toten times the rotor diameter have beenmodeled so far. The multiple refer-ence frames (MRF) model is used toaccount for the rotation of the blades.Blade pitch, wind speed and direc-tion, turbulence intensity and lengthscale, and rotor speed are input foreach simulation.

To validate the CFD model,wake measurements behind a 55 kWpitch-regulated turbine were takenfrom the literature [Ref. 1]. Despitesome inconsistencies in the measuredwind velocities, good agreementbetween the measurements and cal-

culated values was obtained. In addi-tion, calculations presented inReference 1, based on a simpler modelthat did not use the blade geome-try, were not able to predict flow detailsthat were captured by the 3D FLUENT runs. In particular, theenhancement of wind velocity at theedges of the wake could only be pre-dicted by the CFD calculations, eventhough the magnitude of theenhancement was larger than themeasured value.

Once the model was validated,it was used for several investigationsof wake effects. On the previous page,one wind turbine is shown operat-ing in the wake of a second, largerturbine. A wind velocity of 12.5 m/sec,with a turbulence intensity of 13%,was imposed upstream of the front

turbine. Filled contours of constantmean velocity in the plane of the small-er turbine, four diameters behind thefront turbine, show that the veloc-ity field is nonuniform and not cen-tered on the hub. Line contours inthe plane containing the two turbinesillustrate the decay in the wake as afunction of distance behind the tur-bine. These results were used to helpanalyze the special wake loadsexperienced by the rear turbine.

In another example, the excita-tion of vibrations in a power line wasstudied. Wind speeds in the rangeof 1 to 7 m/s and normal to the direc-tion of the power line are most like-ly to cause these vibrations [Ref. 2].If there is a considerable shift in thewind speeds due to wake loadingson the power line, the installation of

The geometry (front) andtypical surface mesh (back)of a turbine rotor and hub

Velocity magnitude slightlydownstream of the rotor plane

wind energy

Velocity magnitude in the wake of a wind turbine

vibration dampers on the power linesmight be indicated. In the case stud-ied, where the power line runs 25mabove the ground, well below theturbine hub, the wake passes overthe power line without causing anyinterference.

Currently, there is little dataavailable for the turbulence intensi-ty in the vicinity of installed wind tur-bines, and this point requires furtherinvestigation. Today, different empir-ical models are used to predict tur-bulence intensity in the wake of windturbines [Ref. 3, 4]. Since these mod-els only predict single averaged val-ues along the wake axis and differfrom one another, they cannot beused to validate the CFD calculations.The distribution of turbulence inten-sity computed by FLUENT in the wakeregion is in reasonably good agree-ment with theory. Absolute values,

however, fall well below measuredturbulence intensities due to effectsnot captured in the current model(e.g. tip vortices and wake mean-dering). Nonetheless, the flexibilityand increased rigor of the CFD cal-culations, when compared to the sim-pler models, suggests that thismethodology can offer improvedinsight into the efficient productionof wind energy in the years to come.

In summary, given the rotor geom-etry and operating characteristics, CFDcalculations are able to predict thewind velocities inside the wake of awind turbine. Specific operating con-ditions, such as pitch angle and rotorspeed, can easily be analyzed. Three-dimensional simulations of wind tur-bines can also be extended to includelandscape topography (see page 8)and other objects located in or nearthe wake. ■


Path lines through the turbine colored by velocity magnitude

References1. 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 energyconverter with variable speed); DEWEK ’92, Deutsche Windenergie-Konferenz 1992;Wilhelmshaven 1993.

2. Degener, T.; Kießling, F.; Tzschoppe, J.; Mindestabstand zwischen Windenergieanlagenund 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, TheNetherlands: ECN Solar & Wind Energy, 1998.

4. Frandsen, St.; Thogersen, L.; Integrated Fatigue Loading for Wind Turbines in WindFarms by Combining Ambient Turbulence and Wakes; Wind Engineering, Vol. 23, No. 6, 1999.

Wind Turbine BladeAerodynamics

by Frank Kelecy, Turbomachinery Application Specialist, Fluent Inc.

Arecent project funded by the Department of Energy (DOE)and the National Renewable Energy Laboratory (NREL) involvedthe study of unsteady blade aerodynamics for large, three-

bladed wind turbines at the National Wind Technology Center (NWTC)in Colorado. The project was one component of a larger effort, fund-ed by the International Energy Agency (IEA) R&D Wind ExecutiveCommittee, where field data was collected and analyzed for windturbines operated by five organizations in four different countries.Because the incoming wind velocities were not, in general, nor-mal to the plane of the rotors, the data collected from all of thesites is considered far more insightful than that taken from windtunnel tests.

At NWTC, a three-bladed, 10m diameter, 20kW Grumman windturbine, operating at a constant speed of 72 rpm, was outfittedwith 155 surface pressure taps on one of the rotor blades. The tapswere used to collect data for incoming wind speed and angle, andfor calculations of turbine power production, and aerodynamic andstructural modes of the rotor.

At Fluent, a simulation has been carried out for one of the NWTCcases, characterized by an inflow wind speed of 7 m/s, using thesteady-state, moving reference frame (MRF) model in FLUENT 6.The geometry of the wind turbine was simplified for the calcula-tion, and consisted of the main blade geometry specified for theNREL turbine (an S809 airfoil) along with an idealized cylindricalnacelle and spinner. The simpler nacelle geometry allowed a sin-gle blade to be analyzed due to the circumferential periodicity ofthe flow. An unstructured mesh was used, consisting of 478,664tetrahedral cells. The computed pressure distribution on the bladeswas used to determine the shaft power, from which the genera-tor power could be derived using available powertrain efficiencydata. The computed generator power and operating efficiency wasfound to be within 1% of test data from the reported power curve.Additional simulations will be performed in order to validate thepresent model over a range of wind speeds. These calculations willserve as a benchmark for others who may wish to pursue wind tur-bine modeling projects with FLUENT 6.

Pressure contours on the surface of theGrumman 20 kWwind turbine

wind energy


wind energy

Awind farm is a plot of land where anumber of wind turbines operate con-currently. The power delivered by the

wind to a turbine is proportional to the sweptarea of the rotor blades and the wind speedcubed. Wind turbines start to generate elec-tricity at wind speeds of about 10 mph, andreach their maximum or rated power out-put at about 33 mph. Depending on thelocation, a wind farm will produce electricityfor about 80-85% of the time, mostly at lowwind speeds. The site of the farm, in par-ticular the topology of the land at and sur-rounding the farm, can play a significant rolein the efficiency of the collective energy out-put of the turbines.

At Renewable Energy Systems in the UK,FLUENT has been used to predict the windspeeds for an existing wind farm at CoalClough, Lancashire. There are 24 turbinesat Coal Clough providing about 6,000 homeswith their electricity needs. The analysis wasdone to generate a “wind map”, or highresolution contour map of wind speeds ata certain height above the ground. The bestwind maps take into account the variationsin the local terrain, including the topogra-phy of the land and the presence of near-by structures. A substantial amount ofmeasured wind speed data was available,and was used for calibration of the CFD results.A well calibrated wind map can provide windspeeds at every location of the wind farmsite. Accurate maps for the surface that slicesthrough the turbine hub centers are essen-tial for planning purposes, especially becauseof the strong dependence of wind speed on power.

For the analysis, a rectangular footprintof land was considered that is oriented inthe direction of the prevailing wind, withsufficient upstream and downstream distancefrom the existing core turbine region. Over160,000 points of terrain height data wereused for a 20km wide strip of land, with aresolution of 50m horizontally and 1m ver-tically. A mesh of one million hexahedral cellswas generated. The grid was progressivelycoarsened in the vertical direction, with thefirst cell layer approximately 0.05m off theground and gradually increasing to 25m inheight at the top boundary of the domain.

The prevailing wind was found to havea height-dependent profile taken fromanemometer measurements at the site ofthe turbines. The measured velocity profileswere applied at the upstream inlet to thedomain through the use of a user-definedfunction. Because the terrain is hilly near thesite of the turbines, the resulting CFD pre-dictions for velocity at the turbine site weregreater than the measured values by about50% in the initial runs. By calibrating theinlet profiles using the measured velocitiesat the turbine, the adjusted predictions atthe turbines were brought to within 10%of the measured values. By repeating thisprocess, using anemometer data fromother nearby turbines and re-calibrating theinlet profiles, the wind speed map was devel-oped into an accurate tool for predicting theflow field at all locations at the site. This proj-ect will allow the company to explore fur-ther the potential of CFD, to improveknowledge of wind conditions at existingand prospective sites. ■

Mapping a

Wind Farmby Joseph K.W. Lam, Fluent Europe

A typical wind map, andclose-up showing thelocations of the turbines

Land topography used as aboundary for the simulations

© Crown Copyright


environmental

In December, Fluent Europe Ltd. held a seminar on CFD in the Water Industry. With the generous assis-tance 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 (59million cubic meters), has a perimeter of 10 miles (16km),and at its deepest is 70 feet (21 meters). The site hasbeen landscaped and considerable effort has been madeto ensure that the public is able to enjoy the beauty andleisure opportunities of Grafham Water, while AnglianWater goes about the business of treating and deliver-ing water to its customers. Grafham Water TreatmentWorks can deliver up to 360 million liters of water a daywith an average daily supply of 230 million liters.

The seminar opened with an introduction and wel-come to Grafham and a general presentation on Fluentand CFD before the day’s proceedings got underway.

Dr. Jim Wicks described some of the major CFD proj-ects that had been undertaken by Anglian Water, andthese included:

• validation of CFD predictions againstlaboratory data for a service reservoir,

• how £60,000 had been saved in pipeworkcosts by using CFD in a service reservoiroptimization study, and

• how a 25% improvement in final water quality had been achieved byrecommending a change in dosinglocation.

Dr. Mike Faram then talked about how FLUENT wasused at Hydro – a leading supplier of novel and inno-vative separation and flow control devices to the world-wide water industry. Their range of products includesthe Hydrobrake® Flow Control, Stormcell® storage media,screening systems such as the Hydro-Jet screen, and arange of hydrodynamic separators such as the Gritking®

and Stormking®.A novel design of combined sewer overflow (CSO)

chamber, the StormFox, was introduced by Russ Currieof Johnston Pipes Ltd. The role of Fluent CFD softwarein fast tracking the design process was discussed.

Following a demo of FLUENT 6.0, the delegates weretaken on a tour of the works, providing a suitable endto a very enjoyable day.

UK Water Seminar on Tap

by Robert Harwood, Fluent Europe

Some of the delegates at the seminar after the touraround Grafham Water Treatment Works, with theanthracite, sand and garnet (ASG) filters in thebackground.

Grafham Water (courtesy of AnglianWater)


chemical

The dispersion of gases in liquids is a process thatis used in the chemical, petrochemical, and phar-maceutical industries for fermentation and oxida-

tion reactions, synthesis, and the manufacture of finechemicals, for example. Stirred tanks, equipped with agas delivering sparger near the base, are typically usedfor this purpose. If the gas flow rate is high, the behav-ior of the gas-liquid mixture differs considerably fromthat of the liquid alone. The power requirements aredifferent as well. While the power required to drive asingle or multiple impeller system is lowered in the pres-ence of the gas, there is an additional power demandto operate the sparger. For optimal gas-liquid mixing,this device should deliver a uniform flow of gas througheach of the many holes that cover its surface.

One of the sparger systems used at LG Chemicals isa continuous stirred tank reactor, driven by two Lightninagitators: an A310 near the top of the shaft and an A320near the base. The reactor has four baffles, a ring-typegas sparger positioned below the A320 with numerousside and bottom holes, side circulation inlets, and anoutlet at the bottom with a vortex breaker and a degassingring. Gas phase reactants are supplied through the sparg-er holes, and liquid phase products are extracted throughthe outlet. A portion of the product stream is recycledto the reactor through the side inlet.

ImprovingSpargerPERFORMANCE

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

The Lightnin A320 and other internalsnear the base of the vessel


Path lines illustrate some of the bubble trajectories

The gas flow in the sparger

For a recent project, several simulations of the reac-tor were performed in an attempt to reduce the pres-sure difference through the sparger holes that had causedan overload problem on some of the compressors. Inorder to accomplish the goal without any loss in pro-ductivity, a decision was made to enlarge the spargerhole sizes. Changing the sparger hole sizes had to becarefully studied, however, because new problems mightbe introduced in the process. Using FLUENT, several aspectsof the planned changes that would be critical to suc-cessfully achieving the goal were checked. First, the flowin the sparger itself was precisely investigated for vari-ous hole sizes. The results were used to assess the dis-tribution of the gas flow rate per hole, and to test whetherthe pressure difference for the gas exiting through theholes was properly adjusted. Next, the liquid flow pat-tern in the reactor was calculated. These results wereused to check for possible problems in the mixing pat-terns in the vessel. As a result of this effort, it was foundthat by modifying the agitator system, a better mixingpattern could be achieved. The revised liquid solutionwas then used as the basis for the gas sparging calcu-lation, which was performed using the discrete phasemodel (DPM). This calculation was used to ensure thatthe hole size proposed in the first phase of the projectwould not lead to any unforeseen problems when thesparger was activated. During this phase of the proj-ect, the underlying assumptions for the DPM were val-idated, and the fundamental concepts for bubble formationby a gas emitted from a sparger hole in a liquid wereinvestigated.

As a result of the project work, the most appropri-ate hole sizes for the spargers was chosen that wouldsatisfy the process goals while introducing no unexpectedproblems in reactor operation. The results also helpedidentify ways to modify other aspects of the agitatingsystem 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. ■

The sparger assembly

chemical


chemical

Static mixers consist of an array of similar, stationary mixing elements,placed one behind the other in a pipe

or channel. Liquids are pumped throughthe channel, and the elements act to accel-erate the homogenization of material prop-erties, such as concentration, temperature,and velocity. In some types of static mix-ers, the elements are rotated by some angle(say, 90°) relative to the previous element.The SMX mixer is one example of this typeof mixer. The elements are complex net-works of angled guide blades, positionedat an angle to the pipe axis, and mixingoccurs through the continuous redirecting,splitting, stretching, and diffusion of the fluids as they pass through the available openings.

Since there are no moving partsinvolved, static mixing occurs with low shear,which is very important for some mixingprocesses where gentle treatment of thematerials is required. Processes of this typeare found in the food processing, phar-maceutical, and biotechnology industries.Static mixers are also widely used in a hostof other industries, however, including oiland gas, chemical processing, polymer pro-duction and processing, and water and wastetreatment. Some of the major manufacturersof static mixers are Sulzer Ltd., Koch-GlitschInc., and Chemineer Inc.

Researchers from the Department ofChemical Engineering at McMaster Universityhave been investigating the laminar mix-ing characteristics of an SMX static mixerusing the discrete phase model (DPM) in

FLUENT. Typically a series of SMX elementsis used to ensure adequate mixing. The mix-ing quality increases with the number ofmixing elements, but so does the powerrequired to pump the fluids through thechannel. For this reason, the number of mix-ing elements used in any given mixer is afunction of the required product quality andoperating budget.

Mixing homogeneity is often rated usingthe coefficient of variation, or COV, whichcan be approximated using the fluid prop-erties, operating parameters, and geome-try of the mixing element. It can also becomputed easily using CFD. Furthermore,CFD can be used to test the COV after thefluid has passed through different elementdesigns, and to determine the minimumnumber of elements required to achieve thedesired product quality. With CFD, theseparameters can be established long beforeconstruction of an experimental apparatusbegins, saving both time and money.

Using FLUENT, COV values, pressure drop,and power requirements have been com-puted for a series of test cases using fourSMX elements in a pipe. Qualitativeresults from the DPM calculations have clear-ly shown the expected stretching and lay-ering of the fluid during the mixing process.Simulations using a two species model totrack the mixing of epoxy resins have alsobeen performed, and the results, particu-larly the species distribution on several axialplanes, are in close agreement with exper-imental data provided by Sulzer for the SMXmixer. ■

Using the species mixing approach, concentration contourson the center plane are shown

Static Mixersby Design

by Shiping Liu, Andrew Hrymak, and Phil Wood, McMaster University, Hamilton, Ontario, Canada; and Rafiqul Khan, Fluent Inc.

Using the DPM, the particle distribution through the mixer,using a central feeding of 20,000 tracers is shown

SMX mixer geometry


aerospace

Afatal accident in July 2000 involving an Air FranceConcorde near the Charles De Gaulle Airport inParis led to the temporary grounding of the entire

fleet of these supersonic passenger planes. An investi-gation into the crash revealed that a metal strip had fall-en off an aircraft previously departing from therunway. When the Concorde taxied over the shard, itstires burst, sending several pieces of rubber flying intothe air. One piece struck the left wing fuel tank of theairplane, rupturing it. The leaking aviation fuel ignitednear the left engine, causing a huge flame to erupt behindthe aircraft. The altered aerodynamics made it impos-sible for the seasoned pilot to control the plane as it lift-ed off from the runway. Tragically, the Concorde crashednear the airport, killing all people on board and someon the ground.

As part of the investigation to explain the accident,researchers at the University of Leeds were encouragedby John Tilston, QinetiQ, who worked on behalf of theAir Accident Investigation Board (AAIB), to look into thereason why the fire stabilized on the wing once it start-ed. They used the VOF model in FLUENT to understandthe flow characteristics of the leaking fuel that gave riseto the observed flame formation. A CFD model of thedelta wing of the Concorde, minus the fuselage, wascreated. (The fuselage was judged to have little or noimpact on the development of the leaking fuel jet.) Severalsimulations were performed using an estimated take-off speed of 100m/s (224 mph) and a range of attackangles that matched amateur photos of the incident.In each model a steady stream of fuel was dischargedinto the CFD domain from a small hole on the under-side of the aircraft wing. Both the k-ε and Spalart-Allmarasturbulence models were employed in the study, bothof which led to similar results.

The FLUENT predictions indicated that a very com-plex, recirculating flow structure developed under thewing as the aircraft lifted off, particularly inside the wheelbay. This result suggested that large recirculating air cellsin the landing gear bay provided a suitably stable attach-ment point for the flame once it was ignited, probablyby an electrical spark. The predicted fuel trajectory wasmainly confined to a small area under the wing that close-ly matched the observed flame in the amateur footageof the crash. This was a qualitative verification of theconclusions drawn by the model. The CFD study, plusother recent studies on how to improve fuel tanks forthe Concorde fleet, has led to modifications that shouldprevent a similar incident from happening in the future.The modified Concorde airliners were reintroduced tocommercial service in October 2001, and the operationalfleet is now fully functional. ■

Predicted CFD cold fuel plume from rupturedleft wing fuel tank during take off

GAMBIT Mesh for the delta wing simulation

FatalConcorde Fire Explained

by L. Ma and M. Pourkashanian, Leeds University (CFD Center), Leeds, Yorkshire, UK, and J. Tilston, QinetiQ, Hampshire, UK

aerospace


aerospace

Modern trains are lighterthan those of past years.This is due in part to the

replacement of a power car at therear of the train with an unpowereddriving trailer. This change has meantlower axle loads, reduced wear onballast, and increased passengercapacity, since the end car can nowbe filled with seats.

For a light-bodied driving trailer,the unsteady aerodynamic loads maybecome significant for the runningbehavior, and this effect has becomea concern for a number of railwayoperators in Europe. In the BriteEuram-funded research project RAPIDE(Railway Aerodynamics of Passing andInteraction with Dynamic Effects), thepartners have joined forces to inves-tigate the boundary layer develop-ment along a modern high-speed trainand the wake flow characteristicsbehind the end car using CFD.

The CFD investigation was divid-ed into three parts, correspondingto three sections of a movingtrain: the front car, the six mid-cars,and the trailing car. The boundarylayer grows in thickness from the frontto the trailing car, and when this thickboundary layer separates behind thetrailing car, the points of separationon the train surface can periodicallyshift. This gives rise to aerodynam-ic oscillations about the longitudi-nal axis, which can cause discomfortto the passengers riding in the trail-ing car. The European organizationsMIRA and SNCF performed bound-ary layer development calculationson the front and mid-car sections,respectively. Their results were thenused by Deutsche Bahn to simulatethe unsteady flow around andbehind the German ICE 2 end car.

The end section modeled was40m in length and positioned in a

Unsteady Flow Behind aby Dr. Christoph Heine and Gerd Matschke, Deutsche Bahn AG, Munich, Germany

ICE 2 end car

Oil-flow path lines, colored by pressure, showthe flow patterns on the end car surface


High Speed Train

domain of length 60m, width20m, and height 15m. A volumet-ric mesh of tetrahedral and prismaticcells was used. The profiles along thesides and on top of the train gen-erated by the other partners in theproject were used as inlet bound-ary conditions. The ground underthe train was given a uniform speedequal to that of the moving train.

A steady-state simulation usingthe k-ε turbulence model was ini-tially performed on multiple proces-sors. The symmetric solution showedlow pressure on the shoulder areasof the end car and a high pressureregion on the back face that resultsfrom the onset of separation. A tran-sient calculation was then initiatedusing the steady solution as a start-ing point. Using time steps of up to0.01s, unsteady flow developed witha period of oscillation on the orderof 1 Hz. This frequency was found

to be in good agreement with meas-urements reported by a Japanese rail-way company1. Further runs weredone using smaller time steps anda higher order turbulence model(RSM), yielding identical oscillationsin the flow. Based on the CFD results,the aerodynamic coefficients werecalculated. These forces and momentsserved as an input for Multi BodySystems (MBS) calculations performedby Bombardier Transportation, andthe running comfort was evaluat-ed. Luckily, the oscillations were foundto be far too weak to cause vehiclemovements, so they would not causeany passenger discomfort. ■

references1 Kohama, Y., Koshikawa, T. and

Okude, Wake Characteristics of a HighSpeed Train in Relation to Tail CoachOscillations, Vehicle AerodynamicsConference, Loughbuough Univ., UK, 1994. steady unsteady

Comparison of surface pressure for the steady and unsteady cases

Path lines and planes showing velocity magnitude contours behind the train

aerospace


sports

Today, victory in sport is a mat-ter of a fraction of a second ora few millimeters separating first

and second place. Therefore any legal,cost-effective, and performance-enhancing technology has to be takenseriously, especially given theamount of money associated withwinning. Whole new scientific dis-ciplines like sports psychology,sports nutrition, and sports bio-mechanics have developed over thelast 30 years, and have become partof the supporting framework behindelite sportsmen and women aroundthe world. During the last five to tenyears, rather late into the fray, sportsengineers and technologists have alsoemerged, and their contributions tothe engineering and technologicalaspects of sports equipment and ath-lete biomechanics have gainedincreasing acceptance. All of thesedisciplines have combined to helpcontinually improve elite perform-ance in sport.

It has long been accepted thatan understanding of fluid flow phe-nomena could lead to performanceenhancements for certain com-petitive sports, especially those dominated by aerodynamics andhydrodynamics. Over the years,

FLUENT has been used for a num-ber of pioneering simulations of thistype, such as motor racing, ski jump-ing, yachting, and sports ball mod-eling. Results have been used tooptimize the balance between dragand downforce (motor racing), toillustrate why one posture is betterthan another (ski jumping), toperfect the design of a winged keel(yachting), and to better understandthe impact of laces and geometricpatterns on flight (sports balls).Performance enhancements that resultfrom analyses like these will undoubt-edly lead to the continued expan-sion of sports engineering in the yearsto come through the use of CFD.

In the United Kingdom, the con-cept of a sports institute, dedicat-ed to understanding and improvingperformance, was first discussed in1995. In October 2000, the ideabecame a reality as the UnitedKingdom Sports Institute (UKSI)opened in London. Sports institutesof this type are not new; many havebeen established around the worldduring the last ten years. All, andespecially the Australian Institute ofSport, have helped contribute tonotable sporting successes. These gov-ernment-funded organizations,

The WinningEdge

by Richard Young, Technology and Innovation Coordinator, UKSI,London, England

Dr. Richard Young at the UKSIcompeted in the sport of cyclingat the 1988 and 1992 Olympicswhile completing a degree inbiomechanics

Olympic cyclists in team pursuit formation Courtesy of the International Sports Engineering Association


which are primarily aimed at help-ing Olympic athletes, seek to pro-vide elite competitors with the facilitiesand leading edge support necessaryto help them excel at the pinnacleof their sport.

It was with this ideal in mind thatthe UKSI has begun to investigatesome of the fundamentals of flowapplications in Olympic sportsusing FLUENT, with the hope of help-ing elite athletes on the BritishOlympic and Paralympic teams. Todate, technological advances haveplayed a major role in manyOlympic sports, such as pole vault-ing, cycling, and skiing, resulting inbetter equipment and refined tech-niques. Many of these advances havenot been systematically studied, how-ever, and some of the underlyingengineering phenomena have neverbeen fully understood. Through theuse of CFD, many of these knowl-edge gaps can be filled. At the UKSI,this technology has been identifiedas having the potential to producesignificant performance gains for eliteathletes. Fluent’s software has beenproven to be successful in other com-petitive sports and is head and shoul-ders better than other CFD codesfor sports applications.

crosswind effects on cyclists

Cycling is one Olympic sportwhere CFD can help illuminate sev-eral flow phenomena. Applicationsfor CFD in this sport are many, includ-ing cycle aerodynamic design,cyclist posture, helmet design, andoptimal cyclist drafting positions dur-ing pursuit races. One area wherecyclists do not agree, however, is onthe selection of rear wheel type ina crosswind. While disk wheelsbecome unmanageable for thefront of a bicycle on windy days, thechoice between disk and the tra-ditional spoked wheels for the rearcontinues to undergo vigorousdebate.

It has been speculated that therear disk wheel could act as a sailin certain circumstances, providinga forward force in the rolling direc-tion opposite the drag force, andhence reducing the net drag expe-rienced by the cyclist. Although manycyclists use rear disk wheels to tryto capitalize on this lift, there hasbeen little clear evidence to supportits existence. An analysis of wheelperformance would add to the grow-ing body of knowledge that CFD hasprovided to date for cycling appli-

cations, much of which cannot beeasily obtained from wind tunnel tests.

In the CFD study carried out, sim-ulations using FLUENT were appliedto a generic geometrical represen-tation of a cyclist and bike createdin GAMBIT. All crosswinds were sim-ulated as constant and steady at 90°to the direction of motion of thecyclist. Calculations were performedfor a cyclist using a spoked front wheelat a forward speed of 25 mph, incrosswind speeds varying from stillair to 30 mph, with spoked and diskrear wheels. Since the same CFDmesh was used for each simulation,it was felt that it should lead to thepredicted trends being accuratelyresolved.

In crosswinds, the cyclist expe-riences a drag force (opposing thedirection of motion) and a side force.While the cyclist only has to workagainst the drag force, the CFD cal-culations showed an increase in themagnitude of the drag force for bothtypes of rear wheels when a cross-wind is present. The net drag forcepredicted by FLUENT as a functionof wind speed shows that in still air,the advantage of using a rear diskwheel over a spoked wheel is neg-ligible (about 2%). As the wind speed

sports


Graph of relative drag difference between a cyclist using a rear wheel with andwithout a disk in a range of crosswinds

increases, however, the advantage ofthe disk wheel improves dramaticallyowing to the “sail effect.” In a 20 mphcross wind, the net drag experiencedby the cyclist is 17% lower with the diskwheel than with the spoked wheel, sug-gesting that the disk wheel gives an appar-ently overwhelming advantage.

There are practical disadvantages todisk wheels though. For example, a diskwheel creates significantly larger sideforces. In a 20 mph crosswind, the sideforce acting on the cyclist plus bicyclewith a rear disk wheel is approximate-ly double that for a cyclist using a spoked

rear wheel. The trade-off for the cyclistis, therefore, one of stability, especial-ly in a gusting wind. In reality, the sit-uation is complicated further byvariability of wind and rolling directions,and shielding by surrounding objects(including, in stage races, the othercyclists). The message from the simu-lations is clear, however. The cyclist canmove moderately to significantly fasterfor the same power output, using therear disk wheel rather than a spokedwheel, confirming the empirical obser-vations experienced by many top-notchcyclists. ■

Flow path lines around a cyclist with a spokedrear wheel in a 20 mph crosswind (top) and adisk rear wheel (bottom)

FlowLab 1.0 is

Released!Virtual Fluids Laboratory

for Engineering Education

Bring the power of CFD to the classroom:

•Reinforce fundamentalconcepts

•Expand lab experiences – easily and economically

•Stimulate interest in fluidmechanics

•Expose students to essential job skills

•Use pre-defined examples or customize your own

[email protected]@fluent.com

sports


appliances

Frost-FreeChilling

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

Mesh scheme of the freezer

Pressure distribution in the freezer

General Domestic Appliances (GDA) Ltd. is the largest man-ufacturer of domestic appliances in the UK, with productsthat include refrigerators, stoves, washing machines, clothes

dryers, dishwashers, and more. GDA began using FLUENT in April2001. The first of their projects to make extensive use of CFD wasthe development of a new line of frost-free refrigeration appliances.

One of the main goals of the project was to design the refriger-ators with improved energy performance, to cut operating costs. Toreduce the energy demands of the units, two aspects of the airflowinside the refrigerators had to be optimized. First, the maximum airflow rate had to be generated using the smallest possible fan. Thiswould not only improve the efficiency, but would also make the unitrun more quietly. Second, the fan(s) and other internals needed tobe positioned in such a way that the airflow inside both the refrig-erator and freezer units was distributed in the most efficient way.Test rigs were constructed so that measurements could be made inparallel with the CFD simulations. The role of these rigs was to val-idate the results of the CFD simulations and carry out the airflowoptimization phase of the project.

The largest freezer studied in this project was 1.8 meters highand had 9 baskets. Because the geometry of the freezer is very com-plicated, with small gaps between the food packs and baskets, a tetra-hedral mesh was used. The results for pressure distribution indicatedthat the largest pressure losses were occurring below and behindthe bottom basket. This result was validated by measurements onthe test rig. After increasing the clearance between the baskets andinside walls, the simulation was repeated, and the total airflow rateof the freezer was found to increase considerably.

The model was also used to study the pack temperature distri-bution in the freezer. A steady-state simulation was performed for acase where the compressor was running 100% of the time, and atransient simulation was performed when the compressor was cyclingon and off. The results for the steady-state case (top right) suggestedthat the top and bottom basket have the warmest pack tempera-ture if the air is uniformly distributed in the freezer. When the com-pressor runs intermittently, however, the top basket has the warmestpack temperature. In order to reduce the pack temperature near thetop and bottom baskets, the simulations showed that more air shouldbe introduced to these regions.

At GDA, FLUENT has been proven to be a useful tool to assistthe development of frost-free refrigerators. It has been used successfullyto identify problems before any prototype models were built. Modelsof other appliances have since been developed and these modelshave provided further useful information for design decision mak-ing, and have assisted in the product development process. ■

Pack temperature distributionin the freezer


appliances

ThermalMapping of a HermeticCompressor

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

Temperature distribution on the internalpump assembly

Path lines illustrate the flow throughthe compressor

Temperature distribution on a vertical planethrough the crankshaft axis

The complex fluid flow and heat transferphenomena in hermetic compressors arevery difficult to analyze theoretically. Because

there is insufficient understanding of the physicsinvolved, assumptions are often made in orderto solve these problems analytically, and theseassumptions can have a negative impact onthe quality of the results. To cope with today’shigh-energy efficiency standards, there is a needto overcome these limitations, so that the flowand heat transfer inside the compressor canbe better understood.

At Kirloskar Copeland in Karad, India, CFDhas been used to perform a more rigorous analy-sis of the entire compressor domain, includ-ing the suction and discharge gas paths. Theability of the FLUENT code to deal with con-jugate heat transfer (conduction and convection)in a turbulent flow encouraged engineers toperform a flow and thermal analysis for theentire compressor. The effort has helped pre-dict such important characteristics as motorwinding temperature, and velocity and pres-sure fields across the domain. The powerfulvisualization tools have made it easy to see theoverall flow patterns along the gas flow paths.

The thermal performance of the compressorplays an important role in the optimal work-ing of the appliance in which it is fitted. Hence,it is necessary to carefully simulate the heattransfer inside the compressor, since it gov-erns the energy efficiency of the whole sys-tem. The most important contributors to thethermal performance are the suction gas super-heating, which is mainly due to heat sources

related to the copper and iron (or core) loss-es and the heat of compression, and volumetricand energy losses occurring in the suction anddischarge gas paths. Other heat sources insidethe compressor are due to rotor and frictionallosses. Each of these effects is represented bya volumetric heat source in the FLUENT model.

To date, the CFD analysis has provided pre-dictions for the temperatures on numerouscomponents inside the compressor. This infor-mation has been used to help design moreefficient motors (with better cooling) and selectthe appropriate Internal Overload Protector(OLP), which protects the motor from over-heating under adverse conditions.

The results of the numerical simulation havebeen validated using an experimental set-upthat uses conventional thermocouples to per-form thermal mapping of the compressor. Thenumerical solution has been found to agreewell with the experimental results. Becausethe simulation resembles the actual testing ofthe compressor on the calorimeter test rig underspecified conditions, the compressor behav-ior can be visualized and thoroughly under-stood well before the prototypes are built andtested. If need be, the compressor design canbe altered to obtain the target performance.The success of the validation work has givenKirloskar Copeland engineers the necessary con-fidence to use CFD during the product devel-opment stage for new equipment, therebyreducing the number of prototypes for trialand error, and the total design cycle time byalmost 30%. ■


electronics cooling

Demands for small and high power sourcesto operate portable electronics and theirassociated accessories are continuing

to increase. Among these demands areincreased power and reduced size for lithium-ion (Li-ion) battery packs and their associat-ed charging units. Li-ion batteries havebecome the power source of choice for portableelectronics because of their high energy den-sity, rate capability, and long cycle-life.However, they tend to self-heat duringcharge and discharge cycles, and lose capac-ity if exposed to or operated at temperaturesgreater than 65°C.

To charge a Li-ion battery, a charger needsto apply a controlled current to increase theLi-ion cell voltage from about 3.0 V to no morethan 4.2 V. Overcharging could lead to capac-ity fading and thermal stability issues. Multi-unit chargers are more economical to operatethan single-unit chargers, but they can run athigher temperatures, causing potential dam-age to the batteries and control electronics.

Motorola Energy System Group (ESG), aleading provider of complete energy systemsolutions for portable electronics, such as cellphones and laptop computers, has used Icepakto address thermal management issues relat-ed to a multi-unit charger for Li-ion batteries.This effort has allowed engineers to simulatethe product’s thermal response for a given setof customer specifications, and confirm or makechanges to the design before a new productis built.

Using Icepak, an eight-unit charger withmaximum natural convection cooling was sim-ulated. Early design validations demonstrat-ed that Icepak predictions of temperature atseveral sites on the charger were in good agree-ment with measured data (see table at right).Through subsequent modeling, it was deter-mined early in the design phase that the cus-

Thermal Modelingof a Multi-Unit Chargerfor Li-ion Batteries

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

The internal peak temperature rise of the charger when fuel gauging (calibrating) eight batteriessimultaneously is shown. The temperature of the load resistors (location 2) rises to ~88°C. Modeling alsoshowed that the heat that evolves mainly from the load resistors causes the temperature of the back of thealuminum (Al) base (location 4) to rise above the critical limit (55°C), set by UL for metallic parts thatcould be touched by the end users.

The table above compares Icepak predictions to experimental data obtained whilethe unit calibrated eight batteries simultaneously

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


electronics cooling

tomer’s time-frame requirement for chargingor calibrating (discharging a fully charged cellfor capacity check) all eight batteries simulta-neously was not possible. The charge step causedthe temperature of the power supply to riseabove its optimum operating temperature.Calibrating affected heat dissipation from theLi-ion cells and their associated load resistors.

Icepak was also used to evaluate the effectsof fan cooling versus fin cooling on the oper-ating temperature of the unit while simulta-neously discharging four batteries andcharging four batteries. Results showed thatthe addition of a fan, meeting cost and designlimitations, provides 15-17% more cooling tosome parts of the charger.

After a number of modifications were test-ed, a final design was chosen. The series ofsimulations showed that the eight-unit charg-er, meeting customer design requirements,is capable of calibrating only three batteries,while charging five at the same time. This opti-mized solution, which includes detailedoperating temperature information for all charg-er components, could not have been obtainedwithout the combined strengths of the ESGengineering staff and Icepak software. The sim-ulations demonstrated not only the limitationsof the existing design, but also alternative solu-tions to improve the thermal performance ofa multi-unit charger. At Motorola ESG, CFDmodeling with Icepak has proved to be a cost-effective tool for predicting the thermal responseof electronic power sources. ■

Fin cooling (top) and fan cooling (bottom) show the temperature distribution onthe outside surface of the charger. In both cases, the simulation was conductedwith four batteries being charged and four batteries being discharged. Bothconfigurations caused the charger to exceed the allowed upper temperature limit(55°C).

This charger has fins placed on thebackside of the printed circuit board(PCB) beneath the load resistors.Additional modifications in this modelincluded increasing the height of theback-wall of the Al-base, and thermalisolation of the back end of the PCBfrom the Al-base. These changes ledto better cooling of the Al-base,maintaining a temperature below55°C.

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For Materials ProcessingFOCUS on CFDFor Materials Processing

Newsletter Supplement











Accelerate Promising FlatPanel Display Research



ARCELOR

CFD:Showerhead in a 300 mm thermal CVD reactorCourtesy of Novellus Systems, Inc.

In background:Concept Two Dual ALTUS™tungsten process chamberCourtesy of Novellus Systems, Inc.











Accelerate Promising FlatPanel Display Research



ARCELOR

S2 Fluent NEWS spring 2002

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The term “materials processing” conjures upan amazingly wide range of applications andindustries, including (but certainly not lim-

ited to) semiconductor manufacturing, glass, poly-mers, non-woven materials, consumer products,food, and metals. The analysis needs of theseindustries are similarly broad, not to mention com-plex: chemical reactions, plasma physics, mul-tiphase flow, radiation, phase change, generalizednon-Newtonian rheology, free surfaces, fluid-struc-ture interaction, porous media, and many more.

Fluent is able to meet these diverse needsthrough a trio of industry-leading products: FLU-ENT, FIDAP, and POLYFLOW. By drawing on theunique strengths of these programs, customersare able to realize the true potential of CFD by:

• reducing the time and expense ofdeveloping new products,

• troubleshooting existing productsand processes,

• decreasing the number ofprototypes needed,

• gaining invaluable physical insightinto their problems.

These benefits have become reality becauseof the tremendous advances in CFD in recentyears, many pioneered by Fluent. One of the maingoals is to improve productivity by reducing thetime required to create the CFD model and obtainthe solution. The direct import of CAD models,extensive use of unstructured meshes, and auto-mated meshing techniques have greatly reducedthe time required for preprocessing. To furtherreduce the turnaround time, more and more usersare taking advantage of parallel processing capa-bilities with multi-processor computers and net-works of workstations. To extend the capabilitiesof the software, many users have taken advan-tage of user-defined subroutines and functions.Specialty modules are available to simulate con-tinuous fiber manufacturing, magnetohydro-dynamics (MHD), and glass batch melting, electrical

boosting, and bubbling (see Product News onpage 34).

Another way that leading edge physical mod-els are incorporated into Fluent’s products is throughpartnerships with technology leaders. In a part-nership with Kinema Research and Software, FLUENT has been linked with the plasma sim-ulation program PLASMATOR® to address plas-ma-enhanced chemical vapor deposition,dielectric and metal etching, ion implantation,and reactor cleaning (see Addressing PlasmaProcessing, Fluent News, Fall 2000). The result-ing 3D simulations are fast enough to allow designiterations in an industrial time frame. The FineParticle Model, developed by Chimera Technologies,allows the simulation of aerosol and hydrosolformation, growth/shrinkage, transport and dep-osition (see Partnerships on page 42). The inte-gration of CFD with flowsheet models is beingaccomplished by a partnership between Fluent,AspenTech, ALSTOM Power, Intergraph, and WestVirginia University in the Vision21 project fund-ed by the U.S. Department of Energy (see Vision

Meeting the Wide-Ranging CFD Needs of

MaterialsProcessing

by Eric Grald, Materials Industry Director, Fluent Inc.

Above, melt blown die for non-wovensmanufacturing: instantaneous flow field (velocityvectors) reveals large scale eddy structure

Below left, temperature differential in a crutcherused for detergent manufacturing

21 Update, Fluent News, Fall 2001). By incor-porating a detailed CFD model (such as a stirredtank reactor) into the flowsheet model of theentire system, engineers can be certain that fluidflow details are accurately accounted for as theprocess is designed and optimized.

The examples in this supplement provide asample of the different ways customers have appliedFluent software to solve their real-world prob-lems. We hope it will offer an appreciation forthe diverse world of applications known as “mate-rials processing.” The future holds many morechallenges in this area, and Fluent is working hardto expand the scope and capability of CFD tomeet these challenges. ■

Fluent NEWS spring 2002 S3

glass

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Gob transferto a blankmold

Gob formationat the feeder

Industrial glass container forming is a complex sequenceof unit processes that leads up to the actual forming processin an individual section machine. The forming process can

be roughly divided into several steps that begin with theformation of a glass gob at the feeder, followed by the trans-fer and loading of the gob into a blank mold. The shape ofthe glass gob and its orientation before it falls are impor-tant components of the manufacturing process of many glassproducts. Large deviations from the ideal gob shape andtrajectory can have severe consequences on the penetra-tion of the glass into the transfer equipment and molds, andasymmetric loading of the gob into the blank molds cancause uneven temperature and wear patterns on the moldinteriors. Traditionally, gob shape control has been conductedby trial and error based on past experience and operatorknowledge, but recent advances in numerical techniquesand computer capabilities have made the numerical mod-eling of the gob forming processes feasible.

A numerical study was performed recently usingPOLYFLOW to investigate the importance of the initial gobformation and transfer on the formation of glass bottles. Thesimulation modeled the formation of the gob at the feed-er, and the transfer of the gob to the blank mold. Techniquessuch as thermo-mechanical coupling, mesh-to-mesh inter-polation, and mesh superposition of the plungers on theglass were employed. Remeshing techniques were used thatallowed a continuation of the calculations despite very severemesh deformations. By evaluating the extent to which feed-er plunger motion and gob transfer equipment affect gobshape and weight, a systematic methodology to control theseparameters can be developed. ■

Reverse-Engineeringa Gob of Glass

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

Ensuring Successful Deliveryof Molten Glass with CFD

by Christopher Jian, Owens Corning, Granville, OH

As the world’s leading glass fiber and materials manufacturer, Owens Corningis 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 vari-ous covered channels and forehearths made of refractory materials. Channels areused 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 tem-peratures by applying cooling or heating along the way. Forehearths are used todistribute glass to each forming station while maintaining glass temperatures dic-tated by the forming products. It is crucial that the front-end system delivers glassof 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, sig-nificant effort has been devoted to the design, engineering, and operation of thesefront-end systems. Engineers at Owens Corning have successfully integrated CFDmodeling in the overall process. Coupled with an in-house computer code, FLUENT is used for modeling both the combustion space and the glass flow. Extensivevalidation of the CFD model against field measurements has been performed, toensure the accuracy and integrity of the simulation results. The CFD model hasbecome an integral tool for improving the design and operation of front-end glassdelivery systems. It is also being used to make engineering and business decisionsthat have resulted in significant capital and operating savings. Currently, this front-end CFD model is being integrated with Owens Corning’s forming technology modelto maximize the potential of numerical simulation. ■

Temperature validation in a channel Temperature validation in a forehearth

Temperature in a fiberglass front-end

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Design Calculator Takesthe Guesswork Out of

HeadlightEngineering

by Eric Jaarda, GE Plastics, Southfield, MI

be selected until the design is ready for analy-sis and its thermal requirements determined. Thisconundrum has encouraged GEP to implementFLUENT in the design process in a new way.

GEP wanted to put the ability to design inits customers’ hands, rather than dictate changesto suit material requirements after the design wasfinalized. To accomplish this, they developed aheadlamp design calculator to assist their cus-tomers in making up-front material selections.Using FLUENT, GEP was able to examine a broadarray of common lamp designs and focus on thedesign features that were most critical to mate-rial selection. The result was a design calculator,soon to be available to GEP’s customers on their

web site www.geplastics.com. The calculatorallows the customer to examine their allowablesystem space before they ever produce any designdata. Instant temperature and material sugges-tions enable them to adjust or trade-off variouselements of their design to achieve a more cost-effective material specification. This is all priorto establishing a final geometry that can thenbe 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 canbe performed, but the initial material sugges-tion from the calculator helps reduce post-designmodifications and speed development. ■

An example of an automotive head lamp reflector

The automotive lighting design calculator

Material selection decisions are becom-ing increasingly critical in automotivelighting. The drive for product differ-

entiation and unique styling has pushed the per-formance envelope of traditional materials. Atthe same time, the demands of the marketplacecontinue to reign in costs and design develop-ment time.

GE Plastics, an engineering resin supplier, hasused FLUENT software to deliver more precisematerial selection guidelines to their automotivecustomers by predicting the operating temperatureof a given headlamp reflector design. Accordingto David Bryce, GEP’s Technical Manager,Lighting, “By selecting the most appropriate mate-rial for each component, our customers can designfor tooling and manufacturing needs that arespecific to that material. Additionally, the low-est cost material meeting the thermal load require-ments can be chosen, averting costs due toover-engineering of the design.” Design-specificheat transfer and fluid flow analysis captures theuniqueness of each lamp system.

Development timelines are continually beingshortened however, and the time required to modeland analyze a complex system can sometimesextend the entire program timeline. “We are find-ing that many of our customers can only allowvery little time in their design cycle for feasibil-ity analysis,” says Jim Wilson, GEP’s CommercialTechnology Manager, High Performance Polymers.A completed design must be immediately sentout for tooling prior to verification that the cor-rect material selection was made. Iterative designchanges are viewed as inefficiency in theprocess. A material choice is needed to optimizethe design, yet the appropriate material cannot


plastics

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ingThe use of thin flexible films for the packaging of

disposable sterile medical devices is a large andgrowing part of the medical packaging market.

In most cases, the packaging format used for med-ical devices is a formed pack produced using a thinpolymeric film sealed to a top web of paper, whichpermits the ingress of the sterilization gas but is resist-ant to bacterial penetration post sterilization. In addi-tion, to keep the cost of the packaging to a minimumand to reduce environmental impact, it is desirable touse as thin a polymeric web as possible. In the caseof a syringe pack, the film thickness may typically be65 – 150µ, reducing to as low as 15 – 35µ in the cor-ners after the thermoforming process. This is adequatefor providing a sterile environment, but may not besufficiently rugged for the life and demands of the pack-aging. For instance, during transit from the manufacturingsite to the end user, it is important that the packageremains intact with no holes or pits forming in the film.A small hole of 10 microns will allow airborne bacte-rial spores to ingress into the pack, leading to a ster-ilization failure.

At REXAM, one of the top consumer packaging com-panies in the world, transit tests have been devisedto simulate and quantify levels of packaging failure forsyringe packs. The rates of failure typically average lessthan 0.2%, with the two primary causes being abra-sion and puncture by the syringe. Failure due to punc-ture was of primary interest to REXAM engineers. Theywanted to develop a technique to predict failure accu-rately and use this knowledge to “reverse engineer”their packaging, so that it would be less prone to punc-ture. The approach they chose involved two compu-tational software packages: POLYFLOW, to model thethickness distribution of the thermoformed pack; andMSC.Marc™, a stress analysis code, to model the strainrate of the thermoformed packaging and predict prob-abilities for puncturing the pack. When combined, thesetwo simulation techniques could be powerful predictorsof mechanical strengths for a given type of syringe pack-aging.

REXAM engineers validated their modeling approachfor a typical 10ml syringe package using two differ-ent film packaging materials. Both films were ther-moformed into a “coffin” style die for the 10ml syringe.In the experimental tests, randomly chosen packs werepunctured using a Lloyd Tensile Tester. CFD modelsfor the two cases were set up in POLYFLOW, using thephysical properties, including the special rheologicalbehavior, of each material used. A membrane approx-imating approach was used to simulate the thermo-forming process in order to reduce the computationaltime required. The CFD predictions were in excellentagreement with measurements for one of the films,and in good agreement for the other. The punctureresistance simulations using MSC.Marc were also invery good agreement with measurements, thus con-firming the suitability of this dual simulation approachfor analyzing this type of film packaging. REXAM believesthat the ability to assess material changes in the pack-aging design will lead to significant time and cost sav-ings in their manufacturing processes in the future. ■

Typical syringe and packaging

PreventingPunctures in Sterile Packaging

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

POLYFLOW CFD simulation of the“coffin” thermoformed productpackaging, showing the thicknessdistribution. Predictions for thicknessat five locations on the coffin surfacewere found to be in very goodagreement with experimentalmeasurements for both materialstested.


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Flow characteristics within the exhaust cup

Sharp Labs Uses FIDAP toAccelerate Promising Flat PanelDisplay Research

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

Sharp Laboratories is the worldwide leader in the development and mass-production of flatpanel displays, otherwise known as thin film transistor LCDs, or TFT-LCDs. Recently therehas been an explosive growth of low-temperature polycrystalline silicon (poly-Si) TFT tech-

nology that promises to deliver novel, high performance, high-content displays. The new con-cept 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 conceptsto materialize, the development of new process technology is needed to understand the com-plex interactions between individual process parameters. Sharp Labs of America is focusing onthe development of such new processes, equipment, and materials to advance the state of LCDtechnology.

One area of particular interest and complexity is the crystallization of amorphous silicon toform poly-Si films. The quality of the poly-Si microstructure impacts the performance of devicesmade with these films and profoundly affects the display capabilities. FIDAP has been used tosimulate the transformation of amorphous-Si thin films to poly-Si through irradiation of the for-mer by a pulsed laser beam. This is a highly complicated process in which the thin film experi-ences ultra-rapid heating, melting, and equally rapid cooling. The process is complicated by severalfactors: phase change occurs far from thermal equilibrium; nucleation occurs in the molten mate-rial as it cools, and the crystals grow and coalesce. A number of modifications have been imple-mented in FIDAP through user-defined subroutines to incorporate these complexities into theexisting phase change model.

Equipped with this advanced simulation tool, the temperature history in the film as a func-tion of the relevant problem parameters can be computed, and the final microstructure withinthe laser-irradiated area can be predicted. The predicted microstructure has been found to com-pare favorably with images of the actual material obtained experimentally. As a result, FIDAPhas been used as a reliable substitute for experimental work to identify promising operating regimesthat optimize the material properties (microstructure). In addition, simulation has been used toinvestigate different irradiation schemes that are either difficult or expensive to implement exper-imentally, unless sufficient evidence exists to warrant the value of the expenditure. As new fea-tures have been added to the model, the value of accurate simulation has proved to be invaluablein the investigation of these highly complex processes. A vast array of operating regimes can nowbe explored without having to resort to tedious and time consuming traditional methods. ■

Optimizing Photo-Resist Film Uniformity

by David Crowley, Tokyo Electron Texas, Inc.,Austin, TX

Temperature history at various locationswithin the film stack

Position and temperature of the solid-liquid interfacewithin the top Si layer as a function of elapsed time

Comparison of simulated (left) and experimental (right) poly-Si microstructure forthe case of laser irradiation that results in random nucleation at the center of theirradiated domain, a scenario that is typically undesirable

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Tokyo Electron Texas, Inc., (TEX) is partof a worldwide organization, Tokyo ElectronLimited (TEL), a leader in semiconduc-

tor and LCD production equipment based inJapan. TEX performs research and develop-ment for Tokyo Electron’s Clean Track systems,which dominate the market because of theirreputation for superior reliability and per-formance.

Clean Track systems are used in the pho-tolithography process that silicon wafers under-go during microchip fabrication. They are usedto coat silicon wafers with a sub-micron thicklayer of photo-sensitive polymer (calledphoto-resist), perform baking and surface prepa-ration processes, send the wafers to a patternexposure tool, and develop the photo-resistafter exposure. The precision of the resultingpattern is strongly dependent on the unifor-mity of the photo-resist thickness across thewafer. This thickness is governed by the waferrotation speed and air flow inside the system,which is driven in part by the design of anexhaust cup, used to remove volatiles. To under-stand the features of two different exhaust cupdesigns, two models of about 1.4 million cellseach were analyzed using FLUENT.

The FLUENT results were in agreement witha simulation done previously by TEX’s par-ent company, Tokyo Electron Kyushu (TKL),which used slightly different boundary con-ditions and other software tools. The resultssupported observations of vortices created athigh spin speeds, giving engineers confidencein the simulation techniques and providingvaluable information related to the modifi-cation of the exhaust cup to improve the sys-tem performance. In the future, FLUENT willbe used to verify improvements to the air-flow and exhaust system designs. ■


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Asyst Technologies, Inc. is theworld’s leading provider ofenvironmental and auto-

mated work-in-progress materialmanagement systems for the semi-conductor manufacturing industry.For the fabrication of integrated cir-cuits, Asyst’s automated wafer iso-lation solutions enable the safe andrapid transfer of wafers betweenprocess equipment and the fabri-cation line, thus increasing pro-duction yield and reducing operatingexpenses. At Asyst, CFD analysis isused for design optimization dur-ing product development, per-formance verification of existingsystems, and troubleshooting of con-tamination problems. CFD hasproven to be a valuable tool in thedesign and analysis of a broad rangeof environmental isolation systems.

As an example of the value ofCFD analysis at Asyst, FLUENT hasbeen used to optimize nitrogen purg-

Geometry of the CFD model. The front surfaces of the FOUPhave been removed to display the wafers. The inlet andoutlet ports are shown on the bottom in blue and green,respectively.

Contours of mass percent of oxygen on a plane through thewafer centers after 40s of purging (not the optimal purgeresults). The FOUP initially has 20.7% oxygen (red) in everyregion.

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

ing of a 300mm front opening uni-fied pod (FOUP). Vapors inside FOUPscan damage wafers during trans-port, storage, and queuing betweenprocesses. For example, moisturecan cause native oxide growth, cor-rosion, and cracking of films, andcontamination by various organiccompounds can degrade the elec-trical properties of integrated cir-cuits. Purging with an inert gas, suchas nitrogen, is a method of remov-ing harmful vapors from FOUPs.

To determine optimal purgingmethods, a CFD model of the sys-tem was developed. A FOUPgeometry filled with 25 wafers wasfirst constructed using Pro/E, andthe model was imported intoGAMBIT for meshing. The FOUP wasinitially set to contain air (with 20.7%oxygen). Pure nitrogen was theninjected through inlet ports on thebottom of the FOUP, and the tran-sient change in vapor concentra-tion was computed. Various injectionand exhaust methods were simu-lated using the same total amountof nitrogen for all cases, to deter-mine the fastest rate of oxygenremoval in all regions of the FOUP.

Examination of a series of oxy-gen contour plots on the centerplane of the FOUP show that theaverage concentration of oxygendrops rapidly over time, and thatregions between the wafers can beeffectively purged within an accept-able time period. Plots of oxygenconcentration vs. time between twowafers show good agreementbetween FLUENT predictions andexperiment. Using CFD as a pre-dictive tool for purging optimiza-tion is less expensive thanexperimentation and provides

detailed concentration results in everylocation within the FOUP.

Work is ongoing at Asyst to fur-ther improve the purging of FOUPsusing FLUENT simulations. Asyst isalso presently using FLUENT fordesign optimization of the next gen-eration of ultra-clean mini-envi-ronments for automated 300 mmwafer handling. ■

Comparison of FLUENT results and data at the center pointbetween the top two wafers (not the optimal purge results)


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The manufacture of steel products is a com-plex process. Demands for improved prod-uct quality have led research centers

dedicated to the steel industry to try to bet-ter understand all phases of the manufacturingprocess. Improved measurement techniquesand numerical simulation are two of the manyareas where the efforts have been directed.At ARCELOR, Europe’s largest steel produc-er and one of the leading steel producers inthe world, FLUENT has been used fornumerical simulations of various steel productionprocesses, many of which involve multiphaseflow. The work has been carried out at IRSID,the company’s central research organization.

purging in a ladleSteel ladles are used for the transport of

molten steel to product forming stations, tem-porary holding prior to the forming opera-tion, chemical addition, and purging.Chemical addition is done to give the steelthe required properties, and purging, usu-ally with jets of argon gas, is done to homog-enize the mixture, both thermally andchemically. It is also used to promote the upwardmotion of inclusions, undesired particulatematter that develops when certain substancesare added. A slag, or layer of impurities, formson top of the molten metal, and by trans-porting the inclusions to the slag layer, theycan be removed with the slag prior to prod-uct forming. Both the discrete phase model(DPM) and the Eulerian multiphase modelhave been used to simulate the purging process,and results are in good agreement with PIVmeasurements on water scale models.

decarburization in avacuum degasser

Vacuum degassing is another process thatis used to purify molten steel. The steel is drawnup from the ladle through a snorkel into avessel held at high temperature and low pres-sure, an environment that helps removeunwanted carbon and dilute gas from the melt.The upward flow is driven by the injection

of argon or oxygen gas. The steel is returnedto the ladle through another snorkel after thedegassing and decarburization have occurred.FLUENT has been used at ARCELOR to sim-ulate the flow field induced by the gas injec-tions, and to study the tracks of carbon andgas particles in the degasser.

cleanliness in continuous casting

The continuous casting process has beenstudied carefully because it is critical to thefinal product quality. Casting is most successfulif there is a gradual yet steady growth of thesolidified shell, with few or no inclusions trappedin the material. An understanding of the flowpatterns in the casting mold is therefore veryimportant, since it is an indicator of the inclu-sion behavior and can be used to evaluatethe effects of argon injection mechanisms andelectromagnetic actuators. The effect of argoninjection can be simulated using either theDPM or Eulerian multiphase model. Inclusions,on the other hand, are best modeled usingthe DPM, since it more conveniently allowsfor a range of particle sizes and densities.Electromagnetic fields have been incorporatedinto the FLUENT simulations using a mod-ule developed at the EPM-MADYLAMLaboratory in Grenoble. The module includesa Lorentz force term in the momentum equa-tions for the melt and particles that has beenfound to contribute not only to the flow pat-terns and particle trajectories, but to the defor-mation of the free surface as well. ■

Steel IndustryApplications atARCELOR

by Jean-Francois Domgin and Pascal Gardin, IRSID, ARCELOR, Maizieres les Metz, France A vacuum degasser, showing the two snorkels at the bottom

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HVAC

In Germany, the occurrence of one severe fire acci-dent at an airport has led officials to review the exist-ing fire protection strategies for airport buildings as

well as those for renovated terminals. Thus when therenovation of Terminal 1 at Frankfurt Airport was planned,fire protection scenarios had to be checked and possi-bly optimized. In order to compare the performancesof different concepts, FLUENT simulations of the orig-inal geometry of the terminal were ordered by the air-port authorities. For validation purposes, experimentsusing a 1:20 model were performed.

The effort began with simulations of the external airflow around the buildings that make up the terminal. Theresults were used to predict static pressures along the outersurfaces of the buildings and at several potential build-ing openings. A second set of simulations focused on firemanagement inside the departure hall of Terminal 1. Ofparticular interest was the time-dependent dispersion

of smoke using different combinations of ventilation fansand openings. Results from the external simulations wereused to identify the optimum locations for fresh air sup-plies for the fire scenarios.

For the external flow simulations, GAMBIT and TGridwere used to build a hybrid mesh of about 4.9 millioncells, based on engineering drawings of the airport build-ings. This model spans a geometric region of 2830 x2830 x 500m3. Surrounding the building of interest, atypical mesh size of 0.9m was used. An exponential pro-file for the wind velocity as a function of height abovethe ground was used as a boundary condition. Two windconditions were considered: one blowing from theNortheast at 3.7 m/s (8 mph), and one from the Southwestat 5.4 m/s (12 mph). The simulations were performedusing the parallel version of FLUENT. All of the exter-nal flow results, even the pressure levels on the build-ing surfaces, were successfully validated through

Smoke Managementat Frankfurt Airport

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

An overview of the buildings at Terminal 1


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 depar-ture hall were found to be different for the different windconditions. This important realization made it clear thatthe smoke management had to be based on a com-bination of fans and natural smoke outlets, rather thanon outlets alone. Fans ensure consistent smoke extrac-tion, independent of exterior weather conditions thatmight compromise the efficacy of the outlets.

The second phase of the project involved an exam-ination of the flow field inside Terminal 1 itself, with theprimary goal being the optimization of the smoke man-agement system in the departure hall. A mixed con-cept of mechanical and natural ventilation systems wastested. The internal geometry was again created in GAM-BIT based on engineering drawings of Terminal 1. Mostof 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 theflexibility of placing trial outlets where needed, this modelwas equipped with openings in many locations. For eachsimulation, the inactive outlets were switched to wallsin FLUENT. The calculations were again performed usingthe parallel solver.

All fire simulations are inherently unsteady. Takinginto account the flow physics, safety requirements, andflow handling devices typically used for fire preventiontasks, a sophisticated time dependent control systemwas 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 thefire begins the extinguishing system is activated and afterten minutes, the fire fighters arrive on the scene.

For the indoor simulations, fires at five different loca-tions were set up following the guidelines of a fire pro-

Static pressure on the outer surfaces of the buildings duringnortheast (above) and southwest (below) wind conditions. Thetall structure at the center alters the pressure on the roof of theadjacent departure hall for the different wind conditions.

HVAC


The geometry of the internal model (left)showing some of the grid detail (right). Thedeparture hall is the area colored green inthe geometry.

tection expert. The fires were modeled as transient sourcesof hot smoke in FLUENT with a number of simplifyingassumptions. Most of the fire simulations were run fora physical time of 8 minutes, using a time step that rangedfrom 0.2 to 4 seconds. In spite of the simplificationsmade, all of the simulations showed good agreementwith experimental measurements from the scaled 1:20 model.

Several optimization runs were performed for the dif-ferent fire locations. During this phase of the project, itbecame evident that dividing the hall volume into activesmoke management segments had a very positive effecton the smoke exhaust, because the fans were loaded withthe nearby smoke and not air. In contrast, attempts todilute the smoke with air had a negative effect. The con-taminated volume merely grew more rapidly and, as aconsequence, more fans with a given volume flow wereneeded to carry the smoke-air mixture out of the hall.

In addition to segmenting the hall, attempting tocreate a layer of smoke in the upper region while keep-ing air in the lower region of the hall was found to be

advantageous, especially near the escape routes. In orderto achieve this, the mixing of smoke and air had to besuppressed and a stable stratification of gases had tobe achieved with a well-chosen combination of venti-lation fans and building openings. To achieve this goal,it was found that windows should not be opened inthe wrong places, and that fresh air supplies in gen-eral should be large enough and far away enough toavoid unwanted mixing.

In the course of the project, several parameters weremodified as the five different fire locations were inde-pendently studied. Special care was taken for regionswith low ceilings, where it was more difficult to createand maintain a thin smoke layer well above the floor.Properly positioned fans and smoke outlets were crit-ical for keeping a nearly smoke-free layer, about 2mthick, on the floor, to allow people to escape safely.

Based on the experimental and CFD results, the air-port management is able to judge renovation meas-ures beforehand in order to maintain a high level ofairport security. ■

HVAC

Improvingthe Air for

Arias


HVAC

Improvingthe Air for

Arias


Path lines, colored by temperature, show differences of up to5°C throughout the seating area and orchestra pit

A view from the stage of the simulated auditorium, showingtemperature contours

by Tamás Régert, Gergely Kristóf, TamásLajos of Budapest University of Technology& Economics, Budapest, Hungary;and Atul Karanjkar, Fluent Europe

Budapest, the capital of Hungary, isone of the most beautiful cities in theworld. One of the jewels in its archi-

tectural crown is the Budapest Opera House,built by Miklós Ybl in 1884 at the heightof the Austro-Hungarian Empire. It has sev-eral ornate decorations that are stunning,and like many public buildings of its vin-tage, was designed with natural ventila-tion in mind. One ventilation feature, forexample, is a central chimney above a largechandelier that hangs from the ceiling.

When a Fluent Europe staff membervisited the local Fluent partner in Budapestlast year, the two went to the Opera Houseto see Tchaikovsky’s Eugen Onegin. It wasa hot day in May, and both felt that thebuilding was quite warm during the per-formance. The experience inspired themto contact the technical manager of theOpera House and introduce him toFluent’s CFD software, a tool that couldhelp find a solution to the building’s ther-mal comfort control problem.

At the beginning of the last century theOpera House’s natural ventilation systemrelied on drafts that were governed by thetemperature differential across the centralchimney. This meant that higher tem-peratures were needed in the upper reach-es of the building in comparison to thecooler temperatures outside. The naturaldrafts acted to draw air up and out of theauditorium. Vents underneath the seatingarea could be opened to permit air to flowinto the auditorium, if needed. During thesummer months, the incoming air passedover ice blocks to provide additional cool-ing. In the past decades the ventilationsystem was modernized several times, withthe last upgrade occurring in the 1980s.During the renovations, forced ventilationand air conditioning systems were intro-duced, and the stage was outfitted witha separate air conditioning system, whichprevents cross-flow between the audito-rium and stage.

Fluent’s Hungarian partner decided tooffer the manager of the Opera House afree HVAC assessment of their building,with the goal of identifying hot spots dur-

HVAC

ing a typical performance. The CFD sim-ulation encompassed the whole audito-rium (minus the stage) with the simulatedeffect of an audience of 1250 heat-gen-erating people and the lights dimmed. Therealizable k-ε turbulence model was usedin the study and full buoyancy effects wereincluded. The CFD results showed that theorchestra pit ventilation was poor in places,a fact that musicians all too readily con-firmed from their own experiences.Thermal anomalies in the balconies werealso correctly identified.

This is not the first time that consult-ants have helped the Budapest Opera House.Fifty years ago, the celebrated scientist LeoBeranek, an expert in the field of acoustics,carried out a sound characterization of thebuilding. His data is still in use today. Fluent’sCFD study will be used in an upcomingreconstruction and modernization of theOpera House, which will include an over-haul of its air conditioning system. Oncethe renovations are complete, opera enthu-siasts in Budapest will no doubt be appre-ciative of Fluent’s CFD efforts for the nextfifty years! ■


automotive

As a full-range systems supplier,Dürr Systems GmbH offersturn-key paint shops for

mass production paint finishing. Thecomplete package contains buildings,plant and environmental engineer-ing, conveyor equipment and con-trol, automation, and materialhandling techniques. Dürr alsooffers a complementary range of man-ufacturing support services for allaspects of the paint finishing process.One important component of theirwork involves CFD simulations.Since 1998, Dürr has used FLUENTto model such things as air flow inspray booths and work stations, air

flow and heat transfer in ovens, mistelimination in scrubbers, response toelectric fields during cathode dip paint-ing, and fluid flow in dip tanks.

Pretreatment is the first of manystages in the painting process. Herethe automotive body is cleaned andprepared for subsequent coatingprocesses using methods appropri-ate for the material involved (steel,aluminium, magnesium, etc). Onephase of the pretreatment process,called phosphating, is used to applya zinc phosphate base coat. Theprocess is normally carried out in diptanks, where the flow is driven by100 to 300 injection nozzles. This coat

protects the body from corrosion andacts as a bonding base. A second-ary reaction produces iron phosphate,which takes the form of sludge andis removed from the dip tank con-tinuously.

When the process is applied toaluminium sections, it triggers a fur-ther secondary reaction, which pro-duces cryolite. To counteract anyreduction in surface quality arisingfrom the presence of cryolite, the flowvelocity should always be above 0.3-0.5 m/sec near the aluminiumcomponents. Since the currenttrend is toward bodies with more alu-minium parts, phosphate tanks in exist-

CustomizedPhosphate Dip Tanks for Cars

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


ing plants often need to be upgrad-ed for new car models in order toincrease the flow velocity at criticalpoints.

CFD simulation is an excellent toolfor optimizing the flow in a phos-phate tank. First, a simulation of oneinjection nozzle is carried out usinga fine grid of approximately 150,000cells. The results are used to gener-ate velocity and turbulence profilesthat are characteristic of the nozzle.Second, a simulation of the completetank is performed using a larger (about2 million cells), yet comparatively coars-er grid. The velocity and turbulenceprofiles predicted in the first simu-lation are used as boundary condi-tions for the injection nozzles in thesecond. The profiles are modifiedslightly to ensure that the jet char-acteristics on the coarser grid are near-

ly identical to those on the fine gridof the first simulation. The secondround of calculations usually requiresseveral days to obtain a suitably con-verged solution, using a 1.0 GHzprocessor. Experiments using tanksfilled with water show good agree-ment with the simulation.

CFD has enabled Dürr to devel-op new dip tanks with optimized flowconditions and offer customers indi-vidual solutions for optimizing exist-ing tanks to suit new car models. The3D simulation plots make it easy forcustomers to understand where theproblem areas lie, and where mod-ifications should be made to obtaina better surface quality. A decline insurface quality can result from poorflow in dip tanks, and can add expenseto customers’ operating costs. In manycases it can be avoided with CFD. ■

Detail simulation of injector nozzle jet

Flow in a dip tank: the side-floodingsystem is illustrated by path lines

Detail of injector nozzle jet simulation

automotive


automotive

The Formula One racing calendar consists of 17 grueling races acrossfour continents. The only goal of each team is for its car to win. Theteams are focused on building racing cars to compete at the pinna-

cle of motorsport. There are many factors that must be considered in design-ing such a highly complex machine. The designers’ aim is to make the carthe fastest around every corner, on every lap. Driver ability, weather con-ditions, 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 max-imize performance. The majority of our team’s work has been in the areaof wing design, with a particular focus on assessing the level of downforceand its effect on the performance of the car. Over half of the car’s totaldownforce is due to the wings. However, the production of downforce comeswith an associated drag force penalty. The aim of the designer is to findwings that generate more downforce with a minimum increase in drag,which on the racetrack could mean the difference between a place on thepodium or not.

Without CFD, many more wing prototypes would have to be constructedand tested, which would be very time consuming and expensive at a timewhen the focus is on ever-shortening design cycle times. Before CFD, allF1 wings were very similar and based on ground-effect wing profiles pub-lished in the public domain. Thanks to CFD, designers can predict exact-ly which performance improvements will accompany every wing shapemodification, no matter how subtle. Indeed, we can usually expect betterthan 90% accuracy on wing element forces before putting them in the windtunnel. FLUENT has also been used to provide accurate load data for ourstress department, and forces on other parts of the car.

One important issue that all Formula One teams must focus on is safe-ty. Ultimately, safety comes first and the Fédération Internationale de l’Automobile(FIA) has laid down clear rules to which each team must adhere. Each raceseason, CFD is used to optimize the racing car design within the FIA reg-ulations. In recent years, it has been particularly useful in reacting to aero-dynamic rule changes that have further limited the size and number of wingelements on the car to reduce cornering speeds.

At Arrows, our long-term objective with CFD software is to carry outmuch more detailed full-car work. We regularly do simulations of 10 mil-lion cells and plan to expand to 30 million cells in the near future as wetackle larger problems and look at existing ones with finer mesh resolu-tion. It is our belief that as FLUENT becomes more widely used on suchareas as radiator cooling flows, it will become as common as CAD on thedesigner’s desktop computer. ■

Arrows Formula 1Team Moving Up The Grid

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


automotive

For the Driver Who

Has Everythingby 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 6.75liter V8 engine, the elegant

Bentley Arnage is an English luxury auto-mobile from the old school. Hand-builtin Great Britain by Bentley Motors of Crewe,now part of the German Volkswagen Group,these luxuriously appointed, individual-ly tailored cars are the most powerfulBentleys ever, capable of speeds up to 155mph with an impressive acceleration of0 – 60 mph in just 5.9 seconds. In addi-tion to its awesome engine, the BentleyArnage is noted for its outstanding ridecharacteristics, minimal internal noise, speed-sensitive steering, and spacious interior.

Recently, Bentley has married the age-old tradition of English engineeringcraftsmanship to advanced technology tofurther perfect its designs. This effort hasresulted in state-of-the-art suspension, trans-mission, braking systems and, thanks toCFD simulations performed by FluentEurope and MSX International, improved

aerodynamics. MSX is a British firm thatprovides engineering solutions for its clients.After evaluating a number of CFD codes,they selected FLUENT to simulate the exter-nal flow around the Bentley Arnage. Thechoice was based on FLUENT’s ease-of-use and flexible meshing capabilities.

The CFD work made use of a center-line symmetric model created withdetailed underhood and underbody res-olution that was subsequently used in abenchmark study. The FLUENT simulationwas adapted to values of y+ and gradi-ents of pressure during the convergenceprocess, and the final CFD predictionsagreed well with experimental meas-urements. Engineers from MSX andBentley look forward to using CFD to tack-le future challenges in the design and opti-mization of the next generation of elitecars. This automotive manufacturer is clear-ly setting the pace at the top end of theautomotive market in more ways than one. ■

External flow around the body and inside the engine bayof the Bentley Arnage: contours of static pressure and pathlines colored by velocity magnitude

Path linescolored byvelocitymagnitudeshowing theflow patternnear theunderbodyof theBentleyArnage

kmh


power generation

Fuel cell technology promises to provide an environmentallyfriendly source of power with broad applications in manyindustries, such as transportation and the military. Among

the current issues surrounding the continued developmentand deployment of this technology is that of manufactur-ing costs. Reduction of manufacturing costs can only be real-ized by optimizing the efficiency of the devices, and this canonly happen through detailed analysis of the complex elec-trochemical and mass transport phenomena taking place.Fluent has developed modeling tools for FLUENT to helpmeet this need, so that engineers can optimize fuel cell designas well as performance. (See the Partnerships section on page42.) As part of this ongoing effort, a user-defined function(UDF) has been developed recently with detailed modelsfor Solid Oxide Fuel Cells (SOFC), a variety that is being tar-geted for distributed power applications, portable power gen-eration (for the military), and auxiliary power units (or APUs,for commercial aircraft).

The SOFC module works in tandem with a FLUENT cal-culation 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 volt-age, and heat flux at the electrodes. This information is thenpassed back to FLUENT, where it is used to update the speciesand temperature fields. The process continues in an itera-tive manner until convergence is reached. In addition to thefuel cell geometry, the operating characteristics include thetotal current output for the fuel cell, which is set as an ini-tial condition. The comprehensive SOFC model, which is fullyparallelized, address the following important processes:

electrochemistryAppropriate chemical reactions for H2 and CO are used

to predict the local current density and voltage distributionsat the electrolyte surfaces. The electrolyte layer is assumedthin for electrochemical modeling purposes (the ionic trans-port across the electrolyte is assumed to be one dimensional),but a finite thickness region in the FLUENT simulation canbe used to represent it. The electrochemical model takesinto account the losses due to activation overpotential (kinet-ic losses), ohmic overpotential (losses due to ionic transportin the electrolyte), and concentration overpotential (lossesdue to to inadequate diffusion of species through the elec-trodes). Binary diffusion coefficients are used to calculate themolecular diffusion of the (gaseous) species throughout thedomain.

potential fieldThis model predicts the current and voltage in all con-

ducting solid and porous regions of the SOFC. Heat gen-erated as a result of ohmic losses in the conducting regionsis also predicted.

The model has been applied recently to a tubular SOFC,where hydrogen and air are used as the fuel and the oxi-dizer, respectively. Fuel utilization is about 80% and the oxi-dizer utilization is about 25%. The total cell current is 11Amp and the average current density is 1850 A/m2. The fig-ures illustrate the non-uniformity in several of the fuel cellvariables that could not be captured by a more simplifiedapproach. ■

The Power of SOFC

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

The current density and voltage on asurface through the electrolyte

Temperature contours on the cathodeside of the electrolyte

air flow in

oxidizer channel

support tube

electrolyte

interconnect

exterior fuel flow

The geometry of the tubular SOFC shows the interconnects ingreen (used to electrically connect a stack of fuel cells), the airinflow and oxidizer channel in red, and the electrolyte in blue.The anode and cathode are cylindrical surfaces on the insideand outside of the electrolyte, respectively.

more.info@[email protected]


power generation

Researchers at the Combustion Technology Sectionof ENEA (Research Center La Casaccia) in Romerecently validated FLUENT through a series of sim-

ulations of the WS Rekumat C-150 B burner. Operationof the 40 kW burner is based on flameless combustiontechnology1, which gives rise to high process efficien-cy with low pollutant emissions. While the burner is designedto operate in either conventional flame or diluted com-bustion (flameless) modes, only the latter was the sub-ject of the present studies. Measurements were madefor three sets of operating conditions, correspondingto process temperatures of 950, 1050, and 1150 K. Thesewere compared to results predicted by FLUENT for thecorresponding conditions.

In FLUENT, two different combustion modelingapproaches were tested: the mixture fraction/pdf method,using an equilibrium assumption, and the Magnussen,or finite rate/eddy dissipation method, using a one-stepreaction mechanism. For both sets of simulations, NOxprediction was performed. The realizable k-ε turbulencemodel was chosen to give the most accuracy for theleast amount of CPU effort, based on earlier benchmarktests performed for similar conditions. Radiation was incor-porated through the use of the discrete ordinates (DO)model.

Both the pdf and Magnussen models gave good qual-itative agreement with the experimental data, with theMagnussen model outperforming the pdf model in itsprediction of centerline temperatures for the low andmoderate process temperature cases. This result sug-gests that at these temperatures, the diluted combus-tion is controlled more by kinetics than by turbulent mixing.The equilibrium assumption at the core of the pdf modelfails to accurately predict the ignition delay in this regime.

At the highest process temperature, the ignition delayis reduced. The Magnussen model overpredicts the delayas well as the maximum temperature. The pdf model,on the other hand, comes closer to predicting the over-all temperature field, even though the maximum tem-perature is again higher than that suggested by themeasurements. This result suggests that turbulent fluc-tuations in the local temperature and mixture fraction,which are better handled by the statistical methods ofthe pdf model, play a more important role in this regimeof operation. Temperature fluctuations were found toplay a significant role in thermal and prompt NOx pro-duction at the higher temperature, as well. ■

Flameless BurnerValidation

by Daniele Tabacco and Claudio Bruno, University of Rome La Sapienza - Department of Mechanics and Aeronautics, Rome, Italy; andGiorgio 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 referencetemperature case, using the one-step Magnussen model

The 3D hybrid mesh used for all simulations

reference1 Wunning, J. A., and Wunning, J. G., Burners for Flameless Oxidation

with Low-Nox Formation Even at Maximum Preheat, Journal of theInstitute of Energy 65, 35-40, 1992.


product news

Three UDF-based add-on modules have been developedfor use with FLUENT 6.0. All three modules handle com-plicated geometry efficiently using unstructured grids,

and are accessible through the graphical user interface. Themodules have been subjected to the same level of testingas FLUENT 6.0, and full documentation and technical sup-port are available.

flow-induced noise predictionThe noise generated by flows across the surface of an

obstruction can be computed using the noise prediction mod-ule. This capability can be applied to the simulation of flow-induced noise in many industries. Some examples includenoise generated by air flowing past the exterior mirror of amoving automobile and noise generated by the flow overlanding gear attached to an airframe. Based on a transientturbulent flow simulation, the time variation of the acousticpressure together with the sound pressure level (SPL) arecalculated using Lighthill’s Acoustic Analogy. The large eddysimulation (LES) turbulence model is highly recommendedfor this purpose, since it can capture the wide band soundspectrum. The model predicts the power spectrum and sur-face dipole strength distribution. Results for flow across aflat plate are in good agreement with experiment data.

magnetohydrodynamic modelingThe interaction between an applied electromagnetic field

and an electrically conductive fluid can be analyzed usingthe magnetohydrodynamics (MHD) module. This capabil-ity can be applied to the continuous casting of steel or alu-minum, for example. The model, an upgrade of the MHDmodel in FLUENT 4, simulates the flow under the influenceof either constant or oscillating electromagnetic fields. A pre-scribed magnetic field can be generated by selecting sim-ple built-in functions or by importing a user-supplied datafile. Coupling between the flow and the magnetic field ismodeled through the induced current (due to the move-ment of conducting material in the magnetic field), and theeffect of the Lorentz (J x B) force as a source term in themomentum equations. The capability is compatible with boththe discrete phase and volume of fluid models. The effectof the discrete phase on the electrical conductivity of themixture can also be included.

continuous fiber modelingIn the fiber spinning process, molten polymer is extrud-

ed through a spinneret, which normally contains hundredsof holes, to form multiple fibers. The fibers are then solid-ified and drawn down in a quenching chamber. The finalfiber strength and quality is strongly influenced by the gasflow field surrounding the fibers, including the rate of con-vective 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 appearedin FLUENT 4. It includes the effect of numerous fibers withcomplete coupling between the fibers and gas flow. Gravityeffects, friction with the surrounding gas, as well as heat andmass transfer are included. The model predicts the effectof fiber motion on the flow field as well as the fiber tem-peratures in the quench box. ■

New Specialty Modules for

FLUENT 6.0by 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 flatplate, as predicted by the flow-induced noise model in FLUENT

A comparison of FLUENT MHD predictions with measurements of normalized steel velocity as afunction of imposed magnetic field at the meniscus of a steel mold. In the simulation, themeniscus velocity changes its direction slowly with increasing field strength, whereas in theexperiment, the meniscus velocity changes its direction more rapidly. The sudden change in theactual casting process is due to the effects of injected argon gas, and these effects were notincluded in the simulation.


product news

The Tenth International Meshing Roundtable con-ference 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 thatis given to a conference poster presenter who has gen-erated a mesh that exhibits innovative technology, andis both eye-catching and technically sound. TedBlacker, the project leader for GAMBIT, was last year’swinner of this prestigious award. His poster also wonthe “Best Technical Poster” award.

The clown grid, one of the examples submitted byBlacker, made use of technology that was developedat Fluent Inc. by Blacker and his colleagues Richard Smith,Yongheng Shao, and Jin Zhu. In particular, new advancesin mesh density controls were used that are now avail-able in GAMBIT. These controls, called size functions,are aimed at eliminating automation obstacles duringmeshing, particularly when generating a tetrahedral mesh.Historically, most volume meshing problems are relat-ed to a bad surface mesh. The problematic surface meshtypically doesn’t capture the geometry well, or isn’t sizedappropriately for thin regions of the geometry. It is alsoparticularly important in CFD analysis that the grada-tion of the mesh be tightly controlled. This control lim-its transition rates from small to large elements, allowingcapture of the boundary layer phenomena as well ascontrol over solution accuracy.

Although density control is not new in the meshingcommunity, this technique is unique in how gradingcontrols radiate or propagate to surrounding regionsin a tightly controlled manner. For example, the eye-brows on the clown have a tight curvature, which is cap-tured through a curvature-based size function. Not onlyis the eyebrow adjusted, however, but portions of thegeometry in close proximity are included in the sizingeffects as well. The forehead near the eyebrow attach-ment and even the interior of the eyelid show a grace-ful, controlled gradation of size. This ensures that thevolumetric tet mesher can successfully fill this region withwell-shaped elements, with minimal intervention by theuser. A simple size function was defined to capture thecurvature and set the gradation rate. This size functionwas attached to the volume and the meshing initiat-ed. The software then generated the needed octree back-ground grid and automatically guided the meshing basedon these controls. (An octree is a hierarchical structureused in certain grid generation algorithms. It begins with

Fluent’s Ted Blacker Wins the

Meshing Maestro Prizeby Dipankar Choudhury, Chief Technology Officer, Fluent Inc.

a coarse background grid that is recursively divided until thedesired grid density is achieved.)

The technical advance that is central to the new con-trols in GAMBIT is accomplished by imposing individual sizefunctions (such as the curvature of individual surfaces) onthe underlying octree-based background grid. The octreedepth (the number of levels in the hierarchy, which corre-sponds to the grid density) adjusts automatically to captureregions of importance in the size function. With the aid ofthe octree background grid, the size functions can then radi-ate beyond the regions where they are defined to accom-plish the control and effects as desired. Three types of sizefunctions are available, and these can be specified individ-ually by the user. The edge, face and volume meshing toolsthen obtain sizing information directly from the backgroundin a highly efficient manner. ■

Ted Blacker and hiswinning surface mesh


computing

The computing potential available totoday’s CFD engineers is nothing short ofremarkable. Ten years ago, only the most

adventurous CFD practitioners used models withmore than 100,000 cells. Many simulations ofthis size could only be solved on the super-computers of the day. Since then, scientists andengineers have scaled up to larger and largerproblems, fueled by ever-faster hardware at steadi-ly decreasing cost. The drop in price of proces-sors and memory has coincided with advancesin software technology to make parallel com-puting within the reach of many companies.For large scale problems, parallel processing algo-rithms have been introduced that allow a cal-culation to be segmented into two or morepartitions that are solved simultaneously on dif-ferent CPUs. Multi-processor workstations, andnetworks of single or multi-processor machinesare now routinely being deployed at compa-nies around the world to make faster work ofsimulations of all kinds using parallel process-ing. Fluent software users are among those whohave taken advantage of this trend, thanks inpart to the robust and scaleable parallel pro-cessing capabilities of the software.

variety of hardwareThere are many ways that a parallel calculation

can be performed. Multi-processor machinescontain two or more CPUs, and can be basedon RISC (running UNIX) or Intel (runningWindows or Linux) architecture. On a dual-processor machine, for example, the two proces-sors share the memory in the system. The sharedmemory enables independent processes to com-municate, using a technique called shared mem-ory processing (SMP1). Single, or serialprocessor machines, which contain only a sin-gle CPU, can be connected over a network toform a cluster. When a network of such machinesperforms a calculation in parallel, the processis called distributed memory processing (DMP).Unlike shared memory processing, where a sin-gle machine manages all the memory, withdistributed memory processing the memoryis managed locally on each machine; here, com-munication among processes occurs over a net-work rather than through shared memory.Multi-processor machines can also be networkedto other multiple or single processor machines.Calculations run on a cluster of this type canuse a process called distributed shared mem-ory processing (DSMP, or often just DSM).

FLUENT UsersCapitalize on

ParallelProcessing

by Liz Marshall, Fluent Inc.

1 SMP traditionally stands for Symmetric Multi-Processor, used as a designation for a system thatsupports shared-memory parallel processing.

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


computing

FLUENT users have employed all of the above approach-es for large jobs in need of parallel processing. RodneyBalzar from Briggs & Stratton Corporation uses a two-processor HP J6000, with 1024 MB of RAM shared byeach processor. His simulations of turbulent flow withheat transfer typically involve more than three millioncells. Jim DeSpirito at the US Army Research Laboratory(ARL) has a large computing facility at his disposal. TheMajor Shared Resource Center at ARL has over 1200 proces-sors on SGI Origin 2000, Origin 3800, and IBM SP super-computers, most of which have hundreds of gigabytesof RAM. DeSpirito’s group is one of many that use thefacility, but he rarely has to wait long in the queue tolaunch jobs. He finds that he gets the best performanceif he sets a limit of about 200,000 cells on each CPU.Thus, jobs involving five million cells typically use 28 to32 processors, while those involving 16 million cells workwell with 64-96 processors. At Hamilton Sundstrand, GaryPost uses a cluster of six dual-processor Dec Alphas, run-ning UNIX, each of which has 4 GB of memory. Themachines are networked to each other, but are segre-gated from the rest of the corporate network. His typ-ical runs, which include combustion and radiative heattransfer, involve from 500,000 to one million cells, areusually done using six processors on three machines. Heoften needs to find six available processors on more thanthree machines, and is grateful for the flexibility that allowshim to choose either one or two from each machine.Giri Manampathy at GE Aircraft Engines usually uses acluster of dual-processor HP workstations. The machinesare linked via a high-speed network, and are segregat-ed from all of the other computers on the company net-work. For problems using up to six million cells, mostof which involve turbulent combustion, he typically makesuse of 12 CPUs on this network. When not using theHP cluster, he can also elect to use an 8-processor sharedmemory PC.

The PC, with Intel-based architecture, has gained pop-ularity among Fluent software users, and indeed, amongengineers and scientists running computationally inten-

sive simulations of all types. For FLUENT users, parallelcomputing is available for both the Windows and Linuxoperating systems and on CPUs from both Intel andAdvanced Micro Devices (AMD). At Babcock Borsig, KenHules uses a cluster of one- and two-processor machinesusing Intel and AMD hardware running Windows. Withtwelve CPUs at his disposal on a high speed networkthat is segregated from the corporate network, he usu-ally 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. Hisproblems are large, in excess of two million cells, butare primarily characterized by complex physics, includ-ing coal combustion and water sprays. Paul Chapmanat Alstom Power also uses a collection of UNIX and PCworkstations, but has added a Linux-based cluster forlarger cases. The cluster has six dual-processor machines,with direct high-speed connections between each of thenodes. It is ideal for larger cases which can exceed twomillion cells, including radiation and chemical reactionsassociated with simulations of large scale power and processequipment. Considering the total cost of running largeCFD simulations, the economics favor running on thefastest possible hardware. For this reason, they have upgrad-ed the hardware twice in the past two years, with thelatest swap to AMD processors running Linux.

performance enhancementsAll of the FLUENT users interviewed have found impres-

sive gains in their computing ability since switching toparallel processing. For Manampathy at GEAE, who hasbeen using parallel processing for about a year, performancehas scaled linearly as he has added compute nodes dur-ing this time. Grid independence is very important tohim, so with parallel processing, he can always ensurethat each solution satisfies this requirement. Balzar atBriggs & Stratton has seen a four-fold improvement afteradding a second node. This exaggerated improvementis most likely due to the fact that his calculations weretoo large to fit inside the available RAM on his serial machine.

“ In addition to its superior accuracy, ease of use andconsistency, FLUENT is also absolutely amazing in itsparallel processing ability. We assembled a smallLinux cluster and obtained a parallel processinglicense. FLUENT performed flawlessly in our clusteredenvironment the first time we tried it. Setting upand running a job in parallel is seamless to the enduser, making FLUENT the ultimate return oninvestment in simulation tools.”

– Ryan HuizengaCAD Systems Supervisor

Litens Automotive Group

continues on page 41 •


computing

By switching to Linux clusters, the CFDgroup at Volvo Aero Corporation has beenable to increase their computational

resources by a factor of ten with a reduced hard-ware budget. The transition from expensiveparallel UNIX machines to large Linux clustershas been a tremendous success, and has ledto huge improvements both in quality and lead-time for all CFD work done.

The CFD group at Volvo Aero were pioneersin using Linux clusters. They bought their firstLinux cluster three years ago, and today havemore than 150 CPUs in the cluster, which isused only for CFD simulations using FLUENTand their in-house CFD code, VolSol. The CFDengineers are very happy with the new com-puting environment. Stability and performancewith FLUENT and VolSol have been marked-ly better than on their old UNIX servers. Becausethe engineers were already familiar with theUNIX environment, the migration to Linux hasgone smoothly. UNIX desktop machines arestill used for most pre- and post-processing work.

Volvo Aero Corporation designs and man-ufactures components for military jet engines,commercial jet engines, and rocket engines.CFD plays an important role in all of these areasand has traditionally been a very strong dis-cipline at Volvo Aero. Most of the work is per-formed at the CFD Center of Excellence, a leadingengineering department that has a long his-tory of CFD experience, and which serves allbusiness units of Volvo Aero. Today there aretwenty-four engineers; one adjunct professor,twelve PhDs, and eleven MScs. The cluster isused only by this group and has made it pos-

sible for them to run a whole new class of prob-lems. Transient, multi-stage turbomachinerysimulations with several million cells are noweasily and routinely run using parallel processingon the cluster.

When the cluster was first assembled thephilosophy was to use as many standard, off-the-shelf components as possible. The com-pute nodes are normal desktop PCs and thenetwork is normal 100Mbs, switched Ethernet.A faster network or non-standard nodes caneasily double the costs. Using standard com-ponents also makes it much easier to main-tain and upgrade the cluster, since mostcompanies already have a well-established chan-nel for buying and maintaining their desktopPCs. New nodes can easily be added as theneed arises and old slow nodes can be removedand re-used as desktop office PCs.

The switch to Linux clusters has also elimi-nated the need for a queue system. The onlytype of scheduling used now is a script that dis-plays the cluster load on a web page. This allowsusers to select available CPUs on an as-need-ed basis. With today’s low cost per CPU, it makesmore sense to buy new nodes as the need aris-es, rather than force users to wait for CPU in aqueue system.

With more than three years of experiencerunning CFD on large Linux clusters, Volvo AeroCorporation has no doubt that this is the com-puting platform of the future. Volvo Aero hasalso started to replace their desktop UNIX machineswith Linux machines – creating a homogenous,low-cost/high-performance computing envi-ronment that can scale to any future needs. ■

Linux Clusters:Inexpensive Power for High-End CFD Computations

by Jonas Larsson, Volvo Aero Corporation, Trollhättan, Sweden

Jonas Larsson in front of the 150 CPU Linux cluster

An air-intakesimulation of a Swedishfighter jet

“We are extremely satisfied with FLUENT’s stability andperformance on our new 150 CPU Linux cluster. Over the three years Volvo Aero has been using Linux clusters, Fluenthas consistently met and exceeded all our expectations. Byswitching to running FLUENT on Linux clusters, we have beenable to increase our computational resources by a factor of 10.”

– Peter Emvin, Ph.D.Manager, Aero and Thermodynamics, Volvo Aero Corporation

A multi-stage axial compressor simulation


computing

The internet and web technologies arecontinuing to revolutionize the waysin which people and organizations com-

municate. The engineering community isnow poised to take advantage of this elec-tronic infrastructure to increase efficienciesin product development processes.Moreover, with even greater improvementson the horizon as a result of higher band-width networks and high performance per-sonal computers, the potential impact onthe design and development process is sig-nificant. For engineering applications,these capabilities will provide the abilityto simulate more complex systems fasterand more efficiently than ever before, using“pay as you go” software on thin clientsthat access remote “compute servers” viathe LAN, a WAN (local- and wide-area net-works, respectively), or over the internet.Thin client systems use centralized serversthat provide application software to userson a network, in contrast to fat client sys-tems, where every desktop has a PC or work-station outfitted with individual installationsof the software. An increasingly popularmethod for deployment of this method usesa Remote Simulation Facility (RSF) that spe-cializes in providing this service to usersthrough the internet.

Using the web as a delivery mechanismfor engineering solutions has significant ben-efits for many end users. Companies canrun simulations affordably, scaling the costof performing simulations to demand, with-out the conventional investment requiredfor software and hardware. A RemoteSimulation Facility arrangement readilyaccommodates the rise and fall of com-putational power needs and solution timerequired for peak periods and lullsbetween jobs. Another benefit of this modelis the potential to increase the rate at whichusers gain access to new software versions.It is not unusual for users to wait nearlyone year for new versions of applicationsoftware to reach their desktop systems.Using the RSF model, new versions can bedeployed more quickly without adverse-ly affecting the desktop user. Further, olderversions can be kept available for users whohave not yet migrated to the newer ver-sion. Administration and support of the appli-cations is more efficient and less disruptive,due to the central nature of this model.

One of the more far-reaching benefitsof a web-based RSF is that it can facilitategreater collaboration between users. Thisis primarily manifested through the cen-tralized nature of the web infrastructure.Specifically, a file located on a web serv-er appears to all users, regardless of theirphysical location, as the same file. This meansthat users will be able to interact acrossgeographical and organizational bound-aries within one company, or across theentire supply chain. An example of this typeof collaboration might be between an auto-motive tier-one supplier and one of the bigthree automotive companies. The suppli-er will use the RSF as a mechanism to runthe exact simulation sequence usingmethods and tools specified by the buyer.

Once the simulation is complete, the resultsand reports become available to the buyeras files on the remote facility, eliminatingthe need to move data from one locationto another. Thus, the RSF becomes the cen-tral point for collaboration, the reposito-ry for shared files, and serves to implementbest practices throughout the simulation.

Another potential area of collaborationis the Simulation Portal. Recent develop-ments in web technologies now allow aweb portal to become a location for groupsof users to develop a cyber-community.For example, users interested in one par-ticular type of simulation could create asmall group that could exchange ideas viaemail about methods and solutions usedto solve specific problems. Using the sameportal, users could also create custom tem-plates for solving application specificproblem types that could be shared anddistributed using access control methods.

When used in combination, a web-basedRemote Simulation Facility integratedwithin a custom Simulation Portal opensup engineering simulation and collaborationto a much larger audience of users thanever before. The implementation of thissolution is generally referred to as anApplication Service Provider model. At firstblush, many seasoned users often dismissthe ASP model as a throw back to the timesof large shared mainframes and high costs.However, with today’s reduced comput-ing costs, and the higher bandwidth prom-ise of the next generation internet, a freshlook at the RSF model is well worth thetime. The challenge for us all is in devel-oping streamlined engineering processesand innovative business strategies that takebest advantage of the new tools for thisgrowing body of potential users. ■

The Impact of the

Web on the EngineeringSimulation Process

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


support corner

More and more FLUENT users are taking advantage of parallel pro-cessing to reduce turnaround time and fully utilize available hard-ware. 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 ofapplications and industries. In this article, some parallel processing basicsare presented along with representative performance statistics and an updateon new parallel processing features in FLUENT 6.0. If you haven’t tried run-ning in parallel yet, this information should convince you to give it a tryand help get you started.

hardware requirementsMany hardware systems today support parallel processing, as shown in

the list below. On these systems, a FLUENT calculation can be shared bytwo 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 theFLUENT user base.

how parallel processing speeds up the calculationIn an ideal world, the time required to run a calculation on two proces-

sors should be half that required to run it on a single processor. Associatedwith this reduction in calculation time, however, is the addition of time requiredto continually communicate information between the processors as the cal-culation proceeds. This computational overhead contributes to the performancerating given to a multi-processor calculation. Each time the number of proces-sors doubles, the computation time on each processor halves, but the over-head continues to increase.

The ideal performance improvement for a parallel calculation is one wherethe performance rating increases linearly with the addition of processors.After several years of dedicated effort, FLUENT is now impressively close tothis ideal for most practical configurations. The graph at left shows the actu-al scale-up of the performance rating for several representative hardwaresystems. The medium-sized benchmark problem used for this set of testsis 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 startedYou can run FLUENT in parallel if your current license allows for two

or more FLUENT processes, and if you have two or more CPUs exclusive-ly available to you. By following the steps outlined below, you can be upand running quickly.

1. Launch the parallel solverThe parallel version of FLUENT can be launched on various platformsusing commands like those shown in the table below.

Getting Started with Parallel Processing

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

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<n> -pnet -cnf=hostfile

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

Performance ratings for a number of representative UNIX and Intel-based systemsshow linear or nearly-linear behavior


In these examples the 3D version of the code is specified(3d), and two processes are started on the multi-proces-sor machines (-t2). For the network examples, a processwill be launched on each machine listed in the hostfile, upto a number <n> specified by the -t flag. For the Windows-based network example, it is assumed that the RSHD com-municator software (included with the FLUENT distribution)has been installed. For instructions on building the host-file file, please refer to Chapter 28 of the FLUENT User’s Guide.

2. Read the grid (or case) file and partitionPartitioning is the task of segmenting your computation-al domain and assigning the segments to individual proces-sors. FLUENT will automatically partition the grid or casefile for you, using defaults that should be close to optimal.If the grid or case has been partitioned previously, the par-titions are retained, but they can be reviewed and adjust-ed at any time. FLUENT provides partition qualityreporting, as well as state-of-the-art partitioning tools.

3. Initialize and solveInitialize and compute the solution as you would in a seri-al (single processor) run. The only difference you shouldsee is faster turnaround!

parallel enhancements in FLUENT 6.0Parallel processing has been available in Fluent products

since the mid-1990s, with improvements highlighted in everymajor release. In FLUENT 6.0, this trend continues, with numer-ous enhancements featured in partitioning controls and flex-ibility. For example:

• Unpartitioned grids can be imported andpartitioned in the parallel solver;

• Stationary non-conformal interfaces can bepartitioned 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) arenow available for use.

Dynamic load balancing (automatic cell migration betweenpartitions) has also been added to help keep your FLUENT ses-sion running optimally. Changes due to local mesh adaption,new loads added to individual processors, and variations innetwork performance for clusters are now managed efficientlyusing 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 UserDocumentation CD, where a Parallel Processing Tutorial is pro-vided to take you through the process in a step-by-step fash-ion. Call your local Fluent office with any questions you mayhave about using this exciting option at your site. Watch out,though. As you cycle through simulations at a faster pace, youmay soon find your workload increasing as your colleaguesapproach you with more and more problems to solve! ■

When this occurs, portions of the cal-culation must continually be swappedout of RAM to the disk so that otherportions can be moved into RAM foractive computation. Swapping, audi-ble by the sound produced when datais written to a hard drive (often a rat-tling sound coming from the computer),can easily slow a calculation down bya factor of two. By adding a secondprocessor and more memory, his cal-culations now easily fit into the avail-able RAM, so his savings have beeneffectively quadrupled. For Post atHamilton Sundstrand, who has beenparallel processing for about two years,larger simulations with a step changein detail are now possible. For a typ-ical combustion problem he usuallyneeded an overnight run to computea cold flow solution. He would thenhave to wait until the following daybefore he could ignite the flame andcompute the final solution. Now, thesetup and cold flow can be done in asingle day, so that the flame solutioncan be performed that night. Whereason a single processor machine, it mighthave taken two and a half days to solvea combustion problem with 200,000cells, it now takes one full day to solveone with over 500,000 cells. Accordingto DeSpirito at ARL, whose simulationscan exceed 10 or even 15 million cells,“Our problems would not be solvablewithout parallel processing.”

Clearly, obvious benefits are real-ized for CFD simulations that rely sole-ly on the solution of transportequations (species mixing and reactions,Eulerian multiphase, transient flow, etc.).The benefits are less apparent whenthe simulation involves particle track-ing and is performed on a cluster.According to Hules at Babcock Borsig,while he achieves linear scale-up

most of the time, the scale-up is reducedwhen he simulates coal combustion.This is because the particle tracking rou-tines currently run at parallel speedson shared memory machines only. (Adistributed memory particle trackingmodel is planned for FLUENT 6.1.)Despite the current limitations, he isstill pleased with the speed-up heachieves when compared to his seri-al runs of the past.

In addition to the benefits offaster processors and algorithms for run-ning calculations in parallel, high per-formance graphics cards have addedthe ability to visualize the results of larg-er models. Where the PC was previ-ously incapable of rendering theresults of large 3D simulations, the fallingcost of 3D graphics hardware hasallowed users to easily manipulate andanimate CFD data, making post-pro-cessing an enjoyable experience.Advances in linking parallel calculationsto real-time desktop post-processingwill allow CFD modeling to extend farbeyond its traditional boundaries in theyears to come.

In today’s engineering landscape,there are increased demands forhigher accuracy from CFD simulations,and these are coupled with demandsfor more rapid turnaround times. Tomeet these demands, parallel processingwill continue to play an ever-expand-ing role. Having evolved from algorithmsfor shared memory workstations to thosefor distributed memory clusters con-necting single and multi-processormachines, parallel processing technologywill continue to grow. Computers willcontinue to stun us as well, with theirincreased power and reduced costs.With these advances, the day will sooncome when problems with tens of mil-lions of cells will become routine. ■

Parallel Processing continued from page 37

computing

Running FIDAP andPOLYFLOW in Parallel

FLUENT is not the only softwarefrom Fluent that takes advantage ofparallel processing. Most of the capa-bilities of FIDAP and POLYFLOW runin parallel on multi-processor machines.

Many platforms are supported, andupcoming releases will continue to focuson improving the usability, perform-ance and robustness of parallel pro-cessing. ■


partnerships

Cooperative Research on Fuel Cells

FLUENT prediction of water vapor mole fraction atthe anode of a PEM Fuel Cell

Fluent CFO Peter Christie (seated, left) and NETL’s LarryHeadley (seated, right) sign the CRADA on PEMFC modeling

Fluent and ICEM CFD Engineeringhave partnered to provide anup-front design tool for per-

forming passenger comfort studiesin automobiles. This easy-to-use appli-cation-specific tool, CABIN MOD-ELER™, developed by ICEM CFDEngineering, allows FLUENT users todefine and mesh the interior geom-etry of a sedan, mini-van, or hatch-back (SUV) by simply definingdimensions on a parameterizedtemplate.

Through its parameterizedapproach, CABIN MODELER™ pro-vides a quick and easy way to per-form climate control studies in theabsence of a CAD file. Parametricdesign studies varying not only com-partment size, shape and angle, butalso register locations, seat clearancesand instrument panel details can bequickly performed throughout thedesign process to ensure optimizedventilation systems and identifypotential design flaws long before

prototypes are built or even detailedCAD data exists.

CABIN MODELER™ creates atetrahedral volume mesh, withoptional prism layers on wall surfaces.Meshing is fully automatic, with defaultmeshing parameters tuned to thegeometry, but with the ability of theuser to override defaults and exertcontrol. The mesh is saved in nativeFLUENT format and can be read direct-ly into the FLUENT solver.

Beyond this, CABIN MODELER™can be a valuable tool in the later stagesof vehicle compartment design. Asdetailed CAD data becomes available,it can be used to facilitate the clean-up and meshing process by merg-ing the actual CAD geometry (e.g.the dashboard) into the parameter-ized template, thus streamlining theprocess from CAD to analysis. ■

Parameterized Model Building for Climate Control

CABIN MODELER™ provides parameterized model building and automatedmeshing for FLUENT automotive climate control simulations


In November 2001, Fluent entered into aCooperative Research and DevelopmentAgreement (CRADA) with the US Department

of Energy’s National Energy TechnologyLaboratory (NETL). This collaboration will focuson the development and validation of a FLUENT-based Polymer Eletrolyte MembraneFuel Cell (PEMFC) model.

NETL and Fluent have agreed to work togeth-er on the development of the PEMFC model,which builds on the existing capabilities of theFLUENT code to calculate fluid flow, heat andmass transfer, and chemical reactions. NETLwill provide expertise in PEM fuel cell technologyto assist in the implementation of submodelsdescribing complex PEMFC physics. NETL willalso work with Fluent in model validation byproviding data from NETL PEMFC experimentsto compare with model predictions and ensuremodel accuracy. The validated PEMFC codewill then be made available to the public aspart of the commercial Fluent software fam-ily. NETL will use the resulting model for theirin-house studies of PEMFC systems. ■

“The result of this CRADA between NETLand Fluent Inc. will be a fully-validated, com-mercial CFD model of the PEMFC, includ-ing electrochemistry, electric field, andmultiphase flow of water. This FLUENT-basedtool will allow PEMFC designers and man-ufacturers to understand the detailedoperation of their PEMFC cell and stack, whichis critical information for design optimiza-tion.”

– Dipankar ChoudhuryChief Technology Officer, Fluent Inc.


[email protected]

or contact Flowmaster Group at +44 1327 306000

[email protected]

[email protected]

Flowmaster GroupAnnounces FLUENTLink

Flowmaster Group has developed an interface between FLUENTand 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 speed1D simulation coupled to detailed 3D analysis from FLUENT.

Typical applications include automotive thermal modeling, in whicha 1D FLOWMASTER simulation can be used to accurately determineflow rates, pressures and temperatures for the external circuit includ-ing components such as the water pump, hoses, radiator, and ther-mostat. This simulation might be coupled to a highly detailed 3D FLUENTsimulation of the flow distribution in the engine cylinder block andheads.

The initial release of FLUENTLink will be available for FLUENT onHP workstations under HP/UX and FLOWMASTER on PCs running Windows®

across a network. Later releases, due before the end of the Q2, willadd further platform support for FLUENT. ■

Aerosol/HydrosolModeling in FLUENT

Fluent partners at Chimera Technologies have developed a new“plug-in” model for FLUENT that addresses aerosol and hydrosolbehavior. Referred to as the Fine Particle Model (FPM), the new

model simulates the formation, growth, transport, and deposition ofparticles in systems influenced by fluid flow, heat transfer, and chem-ical reaction. Applications include chemical reactors, materials processing,pollutant formation and transport, nano-particle sprays, particle inhala-tion and transport, and other systems involving sub-micron particlesin gas or liquid systems.

In contrast to Fluent’s Lagrangian Discrete-Phase Model (DPM), theFPM treats particles in an Eulerian reference frame, and allows parti-cle-particle interactions. It describes the spatial and temporal evolu-tion of the particle size distribution accounting for nucleation and growthof particles, including effects like Brownian motion, adsorption, con-densation, and coagulation. The FPM is a set of User-Defined Functions(UDFs) that work with FLUENT 6. It includes a native FLUENT GUI inter-face and also allows users to modify and extend the model microphysicsand chemistry via their own UDFs.

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

The Fine Particle Model describes the nucleation and growth of particles inaerosol and hydrosol systems, including the influence of chemical reactions, fluidflow, and heat transfer.

partnerships

Fluent has teamed with Soho Corporation, an HPTechnical Computing Channel Partner, to offer customersin North America a fully integrated and tested Linux clus-ter computer system for cost-effective parallel comput-ing with FLUENT. Since its inception in 1996, Soho hasbeen dedicated to the support and implementation ofinfrastructure requirements for technical computing appli-cations. Recently, Soho has worked with Fluent to fullyunderstand the unique requirements of running FLUENT

on a Linux cluster. They assemble the cluster at their facil-ity, and fully configure it for running FLUENT, in a processthat includes installation of the operating system, clus-ter-enhancing applications, drivers, and utilities, and val-idation using FLUENT benchmarks. The completesystem is then installed at the customer’s site, where theFLUENT benchmarks are run a second time. Followingthe installation, Soho serves as the single point of con-tact for cluster infrastructure support management. ■


Turn-key Parallel Computing Solutions


around Fluent

academic news

Italian University Researcher Wins Prestigious Award

November 16, 2001 was an important day for LaBS, the Laboratoryof Biological Structure Mechanics of Politecnico di Milano inMilan, Italy. Dr. Francesco Migliavacca, a member of the LaBS

staff, was awarded Le Scienze Medal for his outstanding accomplish-ments in the study of the haemodynamics after paediatric cardiac sur-gery. His work consists of mathematical modeling using FIDAP and FLUENTsoftware. At the same time Dr. Migliavacca was also awarded a Medalfrom 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 youngresearchers, working in Italy, whose results have been internationallyacknowledged, in recognition of distinguished contributions to the dif-ferent fields of science. This year the conferral ceremony took place atthe Università degli Studi, Milan, and the Awards were presented toDr. Migliavacca (for engineering), Dr. Roberto Bini (for chemistry) andDr. Elena Cattaneo (for medicine) by Prof. Enrico Decleva and Prof. PaoloMantegazza, Rector and Rector Emeritus of the Università degli Studi,respectively. ■

Dr. Francesco Migliavacca

The Maranello, Italy-based Ferrari Formula 1 Teamrecently unveiled their eagerly awaited 2002 racecar to a crowd of 1000 specially invited guests,

including 500 motor racing journalists from the printand televised media around the world. Ferrari’s newcar launch was a predictably glitzy show, to matchthe confidence of the World Champions for the lasttwo years. Gerard De Neuville, Fluent’s Vice Presidentand Manager of Fluent France, was invited to the launchas a Technical Partner of the Ferrari Formula 1 Team.

He was accompanied by Martine De Neuville,Fluent’s Communications Director for South Europe,and Marco Rossi, the Manager of Fluent Italia. In thepicture they are seen beside Luca di Montezemelo,President of Ferrari, and the all-new 2002 race car thatwill be driven again this year by Michael Schumacher,the world champion driver from Germany. After work-ing closely with Scuderia Ferrari for several years, itwas a proud moment, and another motor racing first,for Fluent. ■

Fluent Attends Launch of Ferrari Formula 1 Race Car

Fluent WorldwideFluent Inc.

10 Cavendish CourtLebanon, NH 03766, USATel: 603 643 2600email: [email protected]

US regional officesEvanston, IL 60201Tel: 847 491 0200Ann Arbor, MI 48104Tel: 734 213 6821Santa Clara, CA 95051Tel: 408 522 8726Morgantown, WV 26505Tel: 304 598 3770

Fluent Europe Ltd.Sheffield Airport Business ParkEuropa LinkSheffield, S9 1XU, EnglandTel: 44 114 281 8888email: [email protected]

European regional officesFluent Benelux

Wavre, BelgiumTel: 32 1045 2861

Fluent Deutschland GmbHDarmstadt, GermanyTel: 49 6151 36440

Fluent France SAMontigny le Bretonneux, FranceTel: 33 1 3060 9897

Fluent ItaliaMilano, ItalyTel: 39 02 8901 3378

Fluent Sweden ABGöteborg, SwedenTel: 46 31 771 8780

Fluent Asia Pacific Co., Ltd.Shinjuku Center Building 50F1-25-1, Nishishinjuku, Shinjuku-kuTokyo 163-0650, JapanTel: 81 3 5324 7301Osaka, JapanTel: 81 6 6445 5690

Fluent IndiaPune, IndiaTel: 91 20 6119424

distributorsATES - KoreaBeijing Hi-key Technology Corporation

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

Hungarian Combustion Ltd. - HungaryJ-ROM Ltd. - IsraelLEAP Australia Pty., Ltd.

Australia & New ZealandProcess Flow - FinlandRCCM - Japan (FIDAP & POLYFLOW only)

Scientific Formosa, Inc. - Taiwan(not an Icepak distributor)

SimTec Ltd. - GreeceSMARTtech Services and Systems, Ltd.

BrazilSymKom - PolandTaiwan Auto-Design Company (TADC)

Taiwan (Icepak only)

Techsoft Engineering s.r.oCzech Republic

Thermal Technologies - South Africa

more.info@Visit www.fluent.com

for specific contact information

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