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
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� 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
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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
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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
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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
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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
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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)
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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)
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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
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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
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Adaptative mesh for flow feature extraction
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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
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� 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
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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
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Cooling module simulations on CLOVIS
Fan
Radiator
Air entranceBackplate
Bumper
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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
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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
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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
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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
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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
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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
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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
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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
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parameterized tools
� Experiment and validations still required for alimited number of selected solutions
SC/Tetra
Application Fields
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- 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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