1st Automotive CFD Prediction Workshop Florian Menter, Chief - - PowerPoint PPT Presentation

Florian Menter, Chief Scientist Rob Winstanley, Engineering Manager Domenico Caridi, Senior Regional Product Manager Krishna Zore, Software Developer II Tushar 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

( )

( )

                  +   + − =   +  

j k t j k j j

x k x k C P x k U t k       



( )

( )

                  +   +     + − =   +  

j t j j j k j j

x x x x k F F C P k F C x U t             

   

2

3 2 2 2 1 1

( ),

, max

Real t

C S 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

𝑣𝑗

′𝑣𝑘 ′

→ 𝑣𝑗

′𝑣𝑘 ′ −

𝐷𝐷𝑝𝑠𝑜𝑓𝑠1.2 𝑢 𝑁𝐵𝑌 0.3𝜕, (𝑇2 + 2)/2 𝑇𝑗𝑙𝑙𝑘 − 𝑗𝑙𝑇𝑙𝑘

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

Blend JET MIX MIX

F F C F − =

Wall-Distance Free Variant option available

1 =

Blend

F

• 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-2 GEKO-1

Ahmed Body

SEPARATION AND REATTACHMENT AFTER EXPANSION

Midsection Slant 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

5 15 20 10

Case 2a Coarse – GEKO Csep 1.75 Vs Optimised

Csep 1.75 Optimised Contours of X Velocity at Plane Z = 0

5 15 20 10

Case 2a Coarse – GEKO Csep 1.75 Vs Optimised

Contours of Pressure Coefficent

• 0.9

0.9

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

5 15 20 10

Case 2b Coarse – GEKO Csep 1.75 Vs Optimised

Csep 1.75 Optimised Contours of X Velocity at Plane Z = 0

5 15 20 10

• 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

0.05 0.1 0.15 0.2 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 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

5 15 20 10

Mosaic Vs ANSA Medium – GEKO Csep 1.75

Mosaic ANSA Contours of X Velocity at Plane X = 0

5 15 20 10

Mosaic Vs ANSA Medium – GEKO Csep 1.75

Mosaic ANSA Contours of Pressure Coefficent

• 0.9

0.9

• 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