A combined approach to optimize by simulation the aerodynamic function of the fan system used for engine cooling in automotive application

A combined approach to optimize by simulation the aerodynamic function of the fan system used for eng ine

cooling in automotive application

(6th European Altair Technology Conference)(6th European Altair Technology Conference)

Presented by Dr. Macoumba N’Diaye

(Manuel Henner, Elias Tannoury, Zebin Zhang, Bruno Demory)

Outline

Introduction / Context

Fan optimization through parameterization

High Power Computing

� Remote simulation on external cluster

� Numerical DOE for cooling system

2013 European Altair Technology ConferenceApril 22 nd-24th – Turin, ItalyI 2

� Numerical DOE for cooling system

� URANS and LES for acoustic purposes

� Innovative solution

Conclusions

Introduction / Context

VALEO ENGINE COOLING:� Automotive supplier for cooling module

– Fan Systems– Heat exchangers– Front-end module

� Systems integrator in charge of development

Industrial Partnerships

Automotive Engine Cooling Module


FLUOREM master reseller for Cradle Europe:� Parameterized CFD Software provider

─ Sensitivity studies─ Optimization

� European master reseller for CRADLE solutions� R&D center: partner of major European research

programs in Automotive and Aerospace

Highly demanding thermal specifications forcooling module:� Several types of heat exchangers (radiator, condenser,

Charged Air Cooler, Oil cooler, exhaust gas recirculationfor NOx reduction)

� Compact system integrated in the underhood betweenthe engine and the front face (air entrance, grill,bumper, logo).

Fan systems’ specifications are evolving


Axial position

Pressure

Heat exchangers

Fan system

Engine positionAir entrances

Qv (m3/h)

Fan systems’ specifications are evolvingconstantly:� Strong agility needed to fulfill a wide range of

specifications (from 100 to 1200 Watts)� The willing to have best efficiencies lead to

conduct optimization process for every project� Tough specifications through multi-objectives and

multi-physics requirements (aerodynamics,aeroacoustics)

Fan optimization through parameterization

Parameterized geometryParameterized CAD models :� designers have now the opportunity to build parametric models, allowing them to vary

geometry easily� Until recently and despite the efforts of editors to interface CAD and simulation,

changes were based on the designer's intuition, that checks a posteriori the validity ofthe concept by a simulation or a test.

Aerodynamic profile for a fan blade


Parameterization for the stagger angle (left) and t he camber (right)

Towards improved methodologies for optimization

Methods to support the design process for fan systems� Know-how, standard, procedure, lesson-learned cards are among the

means to help the designer in his choices� Skilled and experimented engineers are needed when the parameters

are numerous and have coupled influences on the aerodynamics.� Experimental designs of experiment (DOE) are sometimes available if

the investment in time and resources could be made. In this case, one


the investment in time and resources could be made. In this case, onecould start considering optimization process.

How can this matter of fact be improved?� Instantaneous assessment of the aerodynamic performance of the fan

after a geometric modification would be the ideal case.� A second step would be to propose the optimized fan regarding to the

targeted operating point

�Is it a Utopia?

Parameterized simulations

Parametric simulation for 2D profiles� One single simulation to provide a result and its derivatives� Databases are build from the reconstructed solutions: any set of parameters is

associated to a solution

Reference simulation « Derived » simulation


Optimization process for the aerodynamic properties� the optimal solution is in the database and corresponds to at least one set of

values of the parameters� A search for an optimum is done by querying the database by a more or less

sophisticated method (Monte Carlo or genetic algorithm), which remains fast (no re-calculation).

Sensitivity analysis for the parameters� Pareto Front can be obtained from the database (all entities that are optimum for

given criteria)� Coupled effects of parameters are highlighted by cross-derivative effect compared to

single parameters� 3 profiles selected according to conditions at various blade span positions (bottom,

mid and top)


Improvement of the solution Bottom

Mid span

Top

Design optimization for 3D cases

3D fan blade optimization� Optimum profiles are re-used to build a blade by stacking profiles from bottom to top� The blade is further optimized by searching for best solutions for stagger angles and

stacking

Stacking

Stagger angles (bottom and top)

Comparison of optimum solutions(choice is an engineering decision)


Stacking

Final accurate performance predictionFan performances predictions� Computational effort limited for last

checking on selected geometries� Equivalent solutions between k-

Epsilon and k-omega turbulencemodel for our cases

� Require of fine mesh (4,8 ME) or agood zonal refinement (240 kE)

Mesh independence study

-100

0

100

200

300

400

500

0 1000 2000 3000 4000 5000

Pre

ssur

e ris

e (P

a)

ExperimentCoarse 40kEMedium 120 kEAdaptatif 120 kEAdaptatif 240 kE


good zonal refinement (240 kE) -100Flow rate (m3/h)

Effect of turbulence model

-100

0

100

200

300

400

500

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Flow rate (m3/h)

Pre

ssur

e ris

e (P

a) Experimentk-eps 4,8MEk-ome 4,8ME

Zonal automated mesh refinement

Adaptative mesh for flow feature extractionTip clearance recirculation� Tip clearance creates a recirculation between the lower side and the upper side of the

blade� This recirculation creates a swirl in the wake� Such a phenomenon is difficult to predict and visualize, since it is convected in the

flow and location is variable� Adaptive mesh is able to densify mesh on such local phenomena


Adaptative mesh for flow feature extraction


High Power Computing

Remote access to computer centers

Contribution to an experimental project supportedby a national research fund (Expamtion)

� Experiments conducted on the new platform CLOVISbased at URCA / Reims


� Access by a web interface

� CFD simulations performed on CLOVIS with 256 parallellicenses of SC-Tetra

� Experimentation of methodologies based on high powercomputing

Simulation management

Submission portal, licenses and queue management� Simulations submitted with an intuitive web interface� Data transfers (upload and download) operated by the system� Licenses and queue management supported by the remote cluster

Usage of the computing power

2013 European Altair Technology ConferenceApril 22 nd-24th – Turin, Italy25/04/2013 I 17

Usage of the computing power� Day to day simulations for engineers� Numerical DOE with a high number of

simulations for cooling module (severalhundred in a limited timeframe)

� Unsteady simulations for acousticpurposes with LES models

Numerical DOE for cooling module


Cooling module simulations on CLOVIS

Fan

Radiator

Air entranceBackplate

Bumper


First Numerical Design Of Experiment :� Fan behavior in actual tiny environment

� Data extraction for aerothermal studiesand aeroacoustic predictions

� Meta-model building with neural network

Distribution of Flow Rate

Distribution of Distance_RAD

0

20

40

60

80

100

120

0 10 20 30 40 50 60

Number of Run

Dis

tanc

e_R

AD

extremes

NOLH and factorial sampling

NOLH

Methodology :� Simulations conducted on CLOVIS (remotecluster) with 256 processors

� ~ 15 millions of elements by simulation

� Automated pre-processing for fastsubmission of the jobs

� Automated post-processing of variousquantities


Distribution of Flow Rate

500

1500

2500

3500

4500

5500

0 10 20 30 40 50 60

Number of Run

Flo

w R

ate

extremes

Parameter distribution(11 parameters in this case)

quantities

Metamodel for global performances

Metamodel based on neural network


Modify values of input parameters to compute new output values .

Input Parameters Values Minimum Maximum Output Paramete rs ValuesAeroResistanceFactor 1 0,5 3,7 Y1_Dp(Pa) 322,14Calage1 68 60 76 Y2_T(Nm) 1,79Calage5 74 66 82 Y3_Eff_analytique(%) 15,85%DistanceBackplate 1000 110 1000DistanceRAD 30 10 106Hmax1 0,08 0,04 0,12Hmax5 0,05 0,01 0,09Lcorde1 65 30 78Lcorde5 75 51 99MassFlow 1500 1000 5000Sweep 100 68 132

Neural network prediction

P1P2P3P4P5P6P7P8P9P10P11

Extension of the method to a sophisticated model

Provide tool for pre-competition ofconcept:� Find the best architecture for a cooling

module and the best fan design

� Take into account roughly effects of airentrance and underhood blockage

� Assess the performances of variousconfigurations


Response surface for 22 parameters :� Neural network for global performances:

Pressure rise, fan torque, efficiency.

� Split the surface of the radiator in 10*10 sub-surfaces and build a response surface for airvelocity on each of them.

� Excel sheet with 22 parameters, 3 outputs forglobal performances, 100 outputs for airvelocity

� Link with Kuli for thermal effect assessment

Unsteady simulations for acoustics


Fan trailing-edge noise (self noise)� Trailing-edge noise is a major source of noise generated by

low speed fans

� Trailing-edge noise, caused by the scattering of boundary-layer vortical disturbances into acoustic waves, occurs at the trailing edge of a lift-generating device

Amiet’s model for broadband noise

z

)x,x,x(x 321====r

turbulence characteristics to know : � The input data are the aerodynamic wall pressure

spectrum , the convection velocity and the


x

yz

)y,x(S ====

0U

2

0

21

0

2

2

20

3 ,,,)(2

),(

′

Φ

=

S

xk

UxL

S

xkd

S

xckxS

cypppp

ωωωπ

ω lr

spectrum , the convection velocity and the transverse correlation scale associated with the incident turbulence (can be extracted from measurements).

� The integral of radiation can be analytically deduced from unsteady aerodynamic theories.

� Wall pressure spectrum at a point near the trailing edge

Large Eddy Simulation on the “Control Diffusion” (C D) profile

Application to an aerodynamic profile


Large Eddy Simulation on the “Control Diffusion” (C D) profile � Valeo test case for simulation validation

� Large amount of data (experimental and numerical) for pressure distribution, boundary layers separation, velocities in the wake, etc…

tip

hub

34

21

5

tip

hub

34

21

5CFD post-processing� Boundary layer data extracted to feed the source model

� Blade span decomposed in strips and airfoil Amiet’s theory applied to each strip

� Wake properties furthermore extracted for Sear’s model (stator noise)

� Full 3D LES for a blade (on-going PhD thesis)

LES on a 2D extruded profile




Numerical approach for new concept design

Benefits of mastering the simulation process:� SC tetra simulations for performance prediction� Easy and fast comparisons between various

configurations� Post-processing and flow analysis to guide

evolution and trigger new ideas� On-going further developments with Fluorem’s

parameterized tools


parameterized tools

� Experiment and validations still required for alimited number of selected solutions

SC/Tetra

Application Fields

2013 European Altair Technology ConferenceApril 22 nd-24th – Turin, Italy

- Robust auto-mesh generator enables capturing complex geometry

- Best-in-class memory saving and computation speed

Application Fields

- Automotive

- Aerospace

- Energy

- Mechanical and Heavy Manufacturing

- Chemical Reaction

Features- Overset mesh- Arbitrary Lagrangian-Eulerian (ALE)- Dynamical motion of element- Heat radiation / Solar radiation- Fan model- Diffusion / Chemical reaction, Combustion

- Multiphase flow / Free surface flow

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