Automotive aerodynamics-optimization---2013-07-17

Details of the Automotive Aerodynamics Optimization work featured in this Bloomberg Businessweek article in the March 11-17, 2013 issue, are presented here

Automotive Aerodynamics Optimization

Sandeep Sovani, Ph.D.

Global Automotive Strategy and MarketingANSYS Inc, Detroit, USAJuly 17, 2012

Ashok KhondgeLead Technology SpecialistANSYS India Pvt Ltd, Pune India

Aerodynamics Optimization Methods

Parametric Method – 50:50:50 Method

Gradient Based Method – Adjoint Method

Agenda

• Method 1 – Parametric Method– Parameterize vehicle shape– Change shape parameters and run numerous simulations– Generate response surfaces and optimize vehicle shape

• Method 2 – Gradient Based Method– Solve for flowfield of baseline shape– Perturb shape and calculate derivative of drag w.r.t. to shape– Identify shape changes yield most improvement in drag

Agenda

• Aerodynamic Optimization via shape exploration– Parameterize vehicle shape– Change shape parameters and run numerous simulations– Generate response surfaces and optimize vehicle shape

• Ideal Aerodynamics Optimization Process– Ability (to explore a large design space)– Automatic (with least human effort)– Fast (Fits in the vehicle development process)– Accurate (High Fidelity Meshes, Physics Models)

Introduction

ThreeEssentials

PAPER 2012-01-0174

HoursDays

NT 50 Design Pts

25 Design Pts

5 Design PtsACCURACY

Introduction

The 50:50:50 Method

50 50 design points in the design space EXTENT

50 50 million cells used in CFD simulation of each design point ACCURACY

50 50 hours total elapsed time to simulate all the design points SPEED

“One – Click” – Entire design space is simulated and post-processed completely automatically after the initial baseline case setup

MethodologyPrepare Meshed Model for

Baseline Vehicle Shape

CFD Solver Setup, Define Shape Parameters

Generate DOE using Input Shape Parameters

Collate Data,Perform Optimization

Morph Vehicle Shape

Run CFD Simulation

STEP 1

STEP 2

STEP 3

STEP 4

STEP 5

• To Demonstrate 50:50:50 Method– Volvo XC60 vehicle model– Four shape parameters– RBF Morph (Integrated within

FLUENT) to define shape parameters

• ANSYS WorkBench (Frame Work to Automate Process)– To drive shape parameters– To create DOE– To perform Goal Driven

Optimization

Test Case Description

Methodology – ANSYS WB

STEP 3

STEP 5

STEP 2

STEP 4

• Surface mesh – ANSA• Surface mesh size

– Front facia : 3.0 to 4.0 mm, – Windshield : 4.0 to 5.0 mm– Doors &windows : 5.0 to 6.0 mm– Roof : 6.0 to 8.0 mm – Rear : 4.0 to 5.0 mm– Underbody : 5.0 to 6.0 mm

Step #1 : Baseline Model Creation

Prism Layer• 10 Layers (First Aspect Ratio 10, Growth 1.1)• 24.4 million cells (about half of total cells are in prism layer

Cell size needed if using cartesian cells with same cell count

Number of cartesian cells needed to achieve same near wall resolution 550 million!

• Volume Mesh – TGrid• Cell Count : 50.2 Million Cells• Prism Layers : 10 (First Aspect Ratio 10,

Growth 1.1)• Prism Count : 24.4 Million Cells• Skewness < 0.9

Prism Layers

Cut Plane Y=0

Cut Plane Z = 1.4 m

• Boundary Conditions– Inlet : Velocity Inlet 100 kmph– Outlet : Pressure Outlet, 0 Pa (Gage)– Side walls : Wall, no-slip– Top wall : Wall, no-slip

• Solver Settings– Steady, PBCS, Green Gauss Node

Based Gradient– Fluid : Incompressible air, – Density = 1.225 kg/m3

– Turbulence : Realizable K-epsilon, Non-equilibrium wall treatment

– Discretization : • Pressure – Standard • Momentum, TKE, TDR – 2nd Order

Step #2 : CFD Setup

• Solution Controls– Courant Number = 200– ERF

Momentum, Pressure = 0.7

– URFs Density = 1.0, Body Forces = 1.0TKE, TDR = 0.8TR = 1.0

Step #2 : RBF Morph

• RBF Morph : Add-On Module– Fully Integrated within ANSYS

FLUENT with GUI/TUI

• Uses Radial Basis Function Technique for Mesh Morphing– System of radial basis function is

used to produce solution for mesh movement

– List of source points and their displacements are used as input

– Valid for both surface shape change and volume mesh smoothing

• Developed by RBF Morph http://www.rbf-morph.com/ RBF GUI

Step #2 : Shape Parameters - Definition

1. Boat Tail Angle (P2)Constraint :

Point “B” to move upto 20 mm in

+ve /-ve Y-direction about the Pivot axis

2. Long Roof Drop Angle (P3)Constraint :

Rear edge to move

Upto 30 mm in +ve Z-direction

Upto 45 mm in -ve Z-direction about the Pivot axis

Step #2 : Shape Parameters - Definition

3. Green House (P4)Constraint:

Point “A” to move 20 mm in

+ve /-ve Y-direction about the Pivot axis

4. Front Spoiler Angle (P5)Constraint:

Point “C” to move 30 mm in +ve Z direction

Axis about which selected surface set gets morphedEncapsulation RegionSurface set selection

Step #2 : Setup – Boat tail angle (P2)

Step #2 : Setup – Long roof drop angle (P3)

Encapsulation RegionSurface set selection Axis about which selected surface set gets morphed

P2 – Boat Tail Angle

P3 – Long Roof Drop Angle

P4 – Green House Angle

P5 – Front Spoiler Angle

• RBF Morph shape parameters– Define in FLUENT – Available in WB for

• Input Shape Parameters– P2 : Boat tail angle– P3 : Long roof drop angle– P4 : Green house – P5 : Front spoiler

• Output Parameter– P1: Drag Force on vehicle

• DOE Algorithm– Central Composite Design– Design Type : Face Centered with

Enhanced Template– 49 DOE Points Generated

Step #3 : Design Space

Design Space BoundsParameter Min Baseline Max

Boat tail angle - 1.85 0.0 + 1.85

Long roof drop angle - 2.30 0.0 + 1.50

Green House Angle - 0.70 0.0 + 0.70

Front Spoiler Angle 0.0 + 3.80

DOE Table

Step #4 : Running Simulations

• Current Study– Simulations were run outside of

WorkBench using journal file in batch mode

– Output the drag force – DOE table updated by

importing output parameters

• Five Runs Using – 768, 384, 288, 240, 144 Cores

• Convergence Monitors– Drag force– Pressure /velocity at few points

in wake

Design point # 1

Design point # 2

Design point # 50

768 Cores 384 Cores 288 Cores 240 Cores 144 Cores

Task Time (Seconds) Time (Seconds) Time (Seconds)

Time (Seconds)

Baseline Case (i.e. Design Point 1)

Read volume mesh of baseline case into the CFD solver and apply solver settings

225 340 365 481 228

CFD Solution 6979 11153 14409 17256 27246

Writing CFD data file 681 538 558 600 532

Each Subsequent Design Point

Morph vehicle shape 84 59 65 69 100

CFD Solution 1284 1754 2208 2630 4100

Writing CFD data file 734 559 572 621 532

Total Run Time (Wall Clock) Needed for All 50 Design Points (Hours)

30.80 35.63 42.98 50.28 72.19

If data file is not written at each data point, then 50 hours target is achieved in less than 200 cores

Same study was repeated with newer hardware. 50 hours target is achieved in roughly 150 cores

Compute Cluster Details

1. Intel’s Endeavor Cluster

2. Intel Xeon X5670 (dual socket)

3. Clock speed 2.93 GHz

4. Six cores per socket (12 cores per node)

5. 24 GB RAM @ 1333 MHz, SMT ON, Turbo ON

6. QDR Infiniband

7. RHEL Server Release 6.1

Updated DOE Table

• Response Surface Analysis– Non Parametric Regression (NPR) Algorithm– Plots

• Goodness of fit• 2d / 3d Response surface plots• Sensitivity plots• Parallel Co-ordinates (Pareto) Plots• Trade-Off plots

• Optimization Study– Goal driven optimization – Screening algorithm (no of samples = 5000)– NLPQL (Non-Linear Programming by Quadratic Lagrangian)

• Flow Results

Results

Results : Response Surface Plots

Results : Sensitivity Plots

Local Sensitivity Global Sensitivity

Results : Parallel Co-ordinates (Pareto) Plot

Results : Trade-off Plots

Results : Goal Driven Optimization

Flow Results

Design Points

Boat Tail Angle(P2)

Long Roof Angle(P3)

Green House (P4)

Front Spoiler Angle (P5)

Drag Force (N) (P1)

1 0.000 0.000 0.000 0.000 388.01

9 0.000 1.500 0.000 1.900 393.01

19 1.850 -2.300 -0.700 0.000 372.30

25 -1.850 1.500 -0.700 0.000 397.33

• Flow Results Discussion– Design point 1, 9, 19 & 25– Velocity contours– Iso-surface of total pressure = 0.0

Summary of 50:50:50 Method

The 50:50:50 Method

50 50 design points in the design space EXTENT

50 50 million cells used in CFD simulation of each design point ACCURACY

50 50 hours total elapsed time to simulate all the design points SPEED

“One – Click” – Entire design space is simulated and post-processed completely automatically after the initial baseline case setup

• The 50:50:50 Method– An extensive, fast, and accurate DOE method– In case study: 4% drag reduction achieved in 1 week of work

• Fully automated workflow (after baseline case setup) using industry leading technologies– FLUENT Solver (With High Performance Computing)– RBF Morph (Fast, User-Friendly, Accurate Mesh Morpher)– ANSYS WorkBench (Integration Platform)– Design Xplorer (Optimization)– CFD Post (Flow Visualization)

Summary of 50:50:50 Method

Agenda

Overview Application Areas and Associated

Technologies R14.5 New Features Solved Examples using R14.5 Summary

Agenda

What is It ?

• Different methods for computing derivatives – sometimes referred to as sensitivities.

• Consider a high-level view of a fluid system

ADJOINT METHOD

Inputs c Quantities qFlow Solver Flow variables

Integral quantities

MeshBoundary conditionsMaterial properties

Transfer matrix j

Tangent method

Adjoint method

Overview of the adjoint method

Workflow

• Solve the flow equations and post-process the results as usual.• Pick an observation that is of engineering interest.

• Lift, drag, total pressure drop?

• Set up and solve the adjoint problem for this observation• Define solution advancement controls• Set convergence criteria• Initialize• Iterate to convergence

• Post-process the adjoint solution to view• Shape sensitivity• Sensitivity to boundary condition settings• Contour & vector plots

Overview of the adjoint methodShape sensitivity: Sensitivity of the observed value with respect to (boundary) grid node locations

nn xwDrag .)(

Shape sensitivity coefficients:Vector field definedon mesh nodes

Node displacement

Visualization of shape sensitivity

• Uses vector field visualization.

• Identifies regions of high and low sensitivity.

• These are the places where changes to the shape can have a big impact on the quantity of interest.

• The guidance is specific to the quantity of interest, and the current flow state.

Drag sensitivity for NACA0012

Associated TechnologiesMesh Morphing

• Use a Bernstein polynomial-based morphing scheme for freeform mesh deformation.

• Select portions of the geometry to be modified by specifying a rectilinear deformation region.

• Define a uniform distribution of control points in space in each coordinate direction.

• Movement of any control point leads to a smooth deformation field throughout the deformation region.

• Can be driven by non-gradient based algorithm – e.g. Simplex.

Increase the downforce on the vehicle

Look for regions of high sensitivity of downforce to shape

Downforce enhancement for a generic race car

Front wing redesign to generate more downforce

Downforce enhancement for a generic race car

Downforce (N)

Geometry Predicted Result

Original --- 425.7

Modified 447.4 (+5.1%) 451.1 (+6.0%)

Rear wing redesign to generate more downforce

Lift enhancement for a generic race car

Downforce (N)

Geometry Predicted Result

Original --- 425.7

Modified 481.3 (+13.1%) 492.5 (+15.7%)

• What are the major factors affecting the uniformity in the mass flow rates at the 4 outlets?

• Material is air

• Solve the flow problem.

• Set up and solve the adjoint problem with the variance in mass flow rates as the quantity of interest.

• Post-process the field to identify important influences.

Robust Design Example: Internal Flow

Total pressure (pascal)

m1: 0.0020 Kg/sm2: 0.0023 Kg/sm3: 0.0028 Kg/sm4: 0.0025 Kg/svar: 7.52e-08

• Variance computed to be 7.52e-08 (Kg/s)^2

• Plot the displacements of the surface that, based on linear extrapolation, would drive the variance to zero.

• Geometry far upstream is dictating the flow split.

• Manufacturing variances of the order of 3mm in the inlet region can cause O(1) flow variance variations.

Robust Design Example: Internal Flow

Surface normal displacements thatInduce O(1) change in variance

Appendix

Design Point #1

Design Point #9

Design Point #19

Design Point #25

Automotive aerodynamics-optimization---2013-07-17

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