1st Automotive CFD Prediction Workshop
Transcript of 1st Automotive CFD Prediction Workshop
Florian Menter, Chief Scientist Rob Winstanley, Engineering ManagerDomenico Caridi, Senior Regional Product ManagerKrishna Zore, Software Developer IITushar Jadhav, Senior Application Engineer
1st Automotive CFD Prediction Workshop
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GEKO - New & Flexible RANS Turbulence Model
Motivation
• Two-equation models are the work-horse in industrial CFD
• The have typically 5 coefficients which can be calibrated to match physics
• They are calibrated for‐ Flat plate boundary layers (log-layer)
‐ Selected free shear flows (plane mixing layer, plane jet)
‐ Decaying turbulence in freestream
• Coefficients are linked and cannot be changed easily by user
Central Question: Can we do such a simulation with one set of global constants?
Probably not …
GEKO Model: Introducing Free Coefficients
( ) ( )
+
+−=
+
jk
t
j
k
j
j
x
k
xkCP
x
kU
t
k
( ) ( )
+
+
+−=
+
j
t
j
jj
k
j
j
xx
xx
kFFCP
kFC
x
U
t
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2
2211
( ),
,max Real
tCS
k
=
• CSEP – changes separation behavior
• CMIX – changes spreading rates of free shear flows
• CNW – changes near-wall behavior
• CJET – Optimizes free jet flows
• CCORNER – Affects corner flows
• CCURVE – Curvature Correction
The functions F1, F2, and F3 contain 6 free coefficients:
All coefficients (except CJET) are UDF functions and can be changed locally
𝑢𝑖′𝑢𝑗
′
→ 𝑢𝑖′𝑢𝑗
′ −𝐶𝐶𝑜𝑟𝑛𝑒𝑟1.2 𝑡
𝑀𝐴𝑋 0.3𝜔, (𝑆2 + 2)/2𝑆𝑖𝑘𝑘𝑗 − 𝑖𝑘𝑆𝑘𝑗
Wall Treatment - Comparison
• The formulation of a turbulence model when integrated through the viscous sublayer is a key aspect of turbulence modelling‐ Defines robustness
‐ Defines accuracy
‐ Can cause undesired pseudo-transition
Makes or Breaks a Turbulence Model
Backstep Simulation
• 4x the same k-e model with different near wall treatment– ML – Menter-Lechner low-Re model
– EWT – Enhanced wall treatment built on 2-Layer formulation
– GEKO-1 exact transformation of k-e to k- with k- wall treatment
– V2F - k-e model with V2F ‘elliptic blending’ wall treatment
• Results are vastly different
• GEKO is closest
Wall Shear Stress Wall Heat Transfer
GEKO Model - Switching
• CSEP - active everywhere
• CNW - active everywhere (but only relevant near wall)
• CMIX – activated by blending function
• CJET – sub-model of CMIX( )...1 BlendJETMIXMIX FFCF −=
Wall-Distance Free Variant option available
1=BlendF
• Incompressible flow‐ Re = 107
• Variation of CSEP and CNW
• Model maintains calibration for wide range of coefficient changes
• CMIX and CJET do not affect boundary layer
Flat Plate Boundary Layer
All 4 coefficients can be tuned by user without loss of accuracy for flat plate
Velocity Profiles for CS0 Diffuser: Cmix=0
Variation of main free coefficients
• CNW – affects only near wall – no effect on Cp
• CSEP – affects separation strength
• CMIX – no effect
• Main parameter - CSEP
CNW=0.5
CSEP=1.0
Separated Flow Around a NACA-4412 Airfoil
Flow scheme Incompressible flow Re = U∞ ∙C/ν = 1.64·106
C - airfoil chord
U∞ - freestream uniform velocity
α = 12o – angle of attack
Triangular Cylinder – Variation of CMIX – Fixed FGEKO
Streamwise velocity contours at the midsection
SST
GEKO-2GEKO-1
Ahmed Body
SEPARATION AND REATTACHMENT AFTER EXPANSION
MidsectionSlant
Incompressible flow
• All the models fail to predict both separation and reattachment on the slant
• Results of GEKO-1 and GEKO-2 are close to the results of their analog
✓ GEKO-1 is similar to k−ε
✓ GEKO-2 is similar to SST
• Results of GEKO-1 and k-e models fit experimental data better than other models
k-ε Std
Best Practice Document - GEKO
https://www.ansys.com/-/media/ansys/corporate/resourcelibrary/technical-paper/geko-tp.pdf
Use 2nd order turbulence when feasible
Summary - GEKO
• A new Generalized k- (GEKO) model has been developed
• It allows optimization of free coefficients over a wide range of applications
• Instead of switching between different models, users can now adjust a single model to their application
• Good chance of consolidation of two-equation models into one optimal format
• Further free coefficients will be added
• Strong defaults
• Coefficients can be changed locally via UDF
• Already successfully used in industrial applications
• Implementation in Fluent (planned for CFX R20)
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Tuning the GEKO Turbulence Model for Case 2a & 2b
Tuning the GEKO turbulence model using Design of Experiment
• Goal is tuning the GEKO‐ To improve the prediction of drag and lift on two and
eventually on more car models
‐ Using main driving parameters and zonal approach
• Car Models used‐ DrivAer Fastback and DrivAer Estate – Corse Ansa Mesh
• Solver Set up‐ Coupled solver, 2nd order Upwind, LSQ, Pseudo Transient
• Parameters used for this study‐ Csep global
‐ Csep local in the wheel MRF zone
‐ Cmix global
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Design of Experiment and Optimization using Workbench DX
• DOE main set up‐ Optimal Space Filling Design
‐ 20 samples
‐ Csep range: [1-2]
‐ Cmix range: [0.3-4]
• Input parameters‐ Csep global, Cmix global,
Csep local (wheel MRF)
• Output parameters‐ dCD, dCL for Fastback and Estate, dCD Fastback-Estate, Mean Square Error
• Total time for one model DOE (20 sim) about 6000 CPU hours‐ Comparable with one scale resolved simulation
• Neural Network Response Surface
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Results
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• Multi Objective Genetic Algorithm‐ Seek for 0 delta for Drag, Lift on both models
‐ Higher priority for Drag
‐ Minimize Mean Square error
‐ Keep same drag trend between two models
Very good trade off improvement!
Case 2a Coarse – GEKO Csep 1.75 Vs Optimised
Csep 1.75 Optimised
Contours of X Velocity at Plane Y = 0
0 5 15 2010
Case 2a Coarse – GEKO Csep 1.75 Vs Optimised
Csep 1.75 Optimised
Contours of X Velocity at Plane Z = 0
0 5 15 2010
Case 2a Coarse – GEKO Csep 1.75 Vs Optimised
Contours of Pressure Coefficent
-0.9 0.90
Csep 1.75 Optimised
-1.00E+00
-5.00E-01
0.00E+00
5.00E-01
1.00E+00
1.50E+00
2.00E+00
-2.00E+00
-1.50E+00
-1.00E+00
-5.00E-01
0.00E+00
5.00E-01
1.00E+00
-1.00E+00 -5.00E-01 0.00E+00 5.00E-01 1.00E+00 1.50E+00 2.00E+00 2.50E+00 3.00E+00 3.50E+00 4.00E+00
Optimised-pressure-coefficient Csep 1.75-pressure-coefficient z-coordinate
Case 2a Coarse – GEKO Csep 1.75 Vs Optimised
Case 2b Coarse – GEKO Csep 1.75 Vs Optimised
Csep 1.75 Optimised
Contours of X Velocity at Plane Y = 0
0 5 15 2010
Case 2b Coarse – GEKO Csep 1.75 Vs Optimised
Csep 1.75 Optimised
Contours of X Velocity at Plane Z = 0
0 5 15 2010
-1.00E+00
-5.00E-01
0.00E+00
5.00E-01
1.00E+00
1.50E+00
2.00E+00
-2.00E+00
-1.50E+00
-1.00E+00
-5.00E-01
0.00E+00
5.00E-01
1.00E+00
-1.00E+00 -5.00E-01 0.00E+00 5.00E-01 1.00E+00 1.50E+00 2.00E+00 2.50E+00 3.00E+00 3.50E+00 4.00E+00
Optimised-pressure-coefficient Csep 1.75-pressure-coefficient z-coordinate
Case 2b Coarse – GEKO Csep 1.75 Vs Optimised
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MosaicTM Meshing – Case 2a
Mosaic (Poly-Hexcore) Meshing
Hex Core• High quality• Fast solve time
New: MosaicTM Technology• Unique technology to
conformally connect poly prisms to hex
• High quality transition, with significantly fewer cells than tet transition
• Patent pending
Poly Prism• High quality• Significantly fewer
cells than tri prisms
Mosaic (Poly-Hexcore) Meshing Parallel – F1 Car
• If Fluent Meshing is opened in parallel Distributed Parallel Meshing will auto-enable
• Particular benefit for large meshes or number of prism layers
• Up to 8.1 Million cells/min with 64-way parallel
• Typical memory requirement: <3 GB / Million cells
Mosaic Remeshing of Medium 2a Committee Grid
• All wall tri-surfaces unchanged• Quads on MFR Internal Surfaces
triangulated and Remeshed
• BOI regions and sizing replicated
• Prism Layers‐ Car - 22 Layers, 1.8e-5m first height,
variable growth rate to Last Ratio 40%
‐ Road - 22 Layers, first aspect ratio 100, 1.17 growth rate
Mosaic Remeshing of Medium 2a Committee Grid
-0.2
-0.15
-0.1
-0.05
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0 50 100 150 200 250 300 350 400 450
CL
Iteration
ANSA (CL mean = 0.0782)
Mosaic (CL mean = 0.0697)
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0 50 100 150 200 250 300 350 400 450
CD
Iteration
ANSA (CD mean = 0.2394)
Mosaic (CD mean = 0.2380)
Mosaic Vs ANSA Medium – GEKO Csep 1.75
• Mosaic creates similar spatial resolution mesh 86M vs 165M cells‐ Parallel meshing on 32 cores completes the
volume meshing in 19 minutes
• HPC Comparison‐ Mosaic➢ 280 cores (14 nodes with 2x Xeon E5-2660 v3 2.6GHZ)
➢ 618.5 CPU.Hours
‐ ANSA➢ 224 cores (8 nodes with 2x Xeon E5-2690 v4 2.6GHZ)
➢ 1422.4 CPU.Hours
• Similar accuracy 2x speed up‐ Similar results with ANSYS Hexcore meshes
Mosaic Vs ANSA Medium – GEKO Csep 1.75
Mosaic ANSA
Contours of X Velocity at Plane Y = 0
0 5 15 2010
Mosaic Vs ANSA Medium – GEKO Csep 1.75
Mosaic ANSA
Contours of X Velocity at Plane X = 0
0 5 15 2010
Mosaic Vs ANSA Medium – GEKO Csep 1.75
Mosaic ANSA
Contours of Pressure Coefficent
-0.9 0.90
-1.00E+00
-5.00E-01
0.00E+00
5.00E-01
1.00E+00
1.50E+00
2.00E+00
-2.00E+00
-1.50E+00
-1.00E+00
-5.00E-01
0.00E+00
5.00E-01
1.00E+00
-1.00E+00 -5.00E-01 0.00E+00 5.00E-01 1.00E+00 1.50E+00 2.00E+00 2.50E+00 3.00E+00 3.50E+00 4.00E+00
ANSA-pressure-coefficient Mosaic-pressure-coefficient z-coordinate
Mosaic Vs ANSA Medium – GEKO Csep 1.75
Summary
• Tuning GEKO allows a RANS model to get much closer to experimental results ‐ Running a DoE for two vehicle configurations takes similar CPU resource to a single scale
resolving simulation
• Mosaic Parallel Meshing creates a mesh with similar spatial resolution to traditional Prism-Tet-Hexcore meshes with 40% – 50% less cells‐ This results in a 2x speed up in solution time with not loss of accuracy
• Mosaic Parallel Meshing generates 86 Million cells in 19 Minutes.
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Thank You and Questions