Aerodynamics Optimization - Ansys · PDF filedesign exploration ... optimizer, cut‐cell...

Aerodynamics Optimization

Sandeep Sovani Ph.D.

Manager Global Automotive Market Strategy

© 2011 ANSYS, Inc. September 22, 20111

Manager, Global Automotive Market Strategy

ANSYS, Michigan

Brief History of Aerodynamics CFD

2000s1980s1970s 1990s


Brief History Aerodynamics CFD

2000s1980s1970s 1990s

What is CFD? Does it really work?

Does it work for my application

Can it be used in production?

(aerodynamics)?

2010s – How to leverage simulation extensively?


Decade of Optimization

CFD in Aerodynamics Development

The main purpose of CFD is to enable optimization via thoroughThe main purpose of CFD is to enable optimization via thorough design exploration

Thorough Design Exploration

Design Alternatives Deep Insights Multiple Aspects

Ability to evaluate performance of numerous design alternatives in a h i li l

Ability to provide in‐depth understanding of physical phenomena affecting

f

Ability to study each design alternative from all performance aspects ( d li flshort timeline, at low cost performance (e.g. drag, cooling flow, wind noise, etc)


CFD in Aero Development

Current state‐of‐the art (commercial, non‐commercial codes)

Design Alternatives Deep Insights Multiple Aspects

State‐of‐the‐art –• Few engineer days

State‐of‐the‐art –• Codes generally accurate

State‐of‐the‐art –• Large models needed for• Few engineer‐days

needed for studying each design alternative

• Codes generally accurate for A‐to‐B comparison• Advanced post‐processing

• Large models needed for all‐in‐one simulation (aero, underhood, wind noise)

Further Needs –• Ability to simulate detailed aerodynamics for

Further Needs –• Some accuracy issues lingering (e g lift

Further Needs –• No special needs. Hardware/softwaredetailed aerodynamics for

a large DOE matrix, fast (e.g. 50 design alternatives in a weekend)

lingering (e.g. lift calculation)• Debate about need for transient calculation

Hardware/software growth will allow co‐simulation


Technological Building Blocks f A d i O i i i

Ability to simulate detailed aerodynamics for large DOE matrix in a short time (e g simulating 50 design

for Aerodynamics Optimization

matrix in a short time (e.g. simulating 50 design alternatives automatically over a weekend)

Major technological advances needed in –1. Model creation

2 S l d2. Solver speed

3. Process management

4. Data management

ANSYS is first in overcoming these technological challenges


Tech. Building Blocks for Aero Optimization1 M d l C ti

Model Creation

1. Model Creation

Surface Wrapper Cut Cells

Model Creation

Cut Wrap Morpher

Reference:SAE Paper 2009‐01‐0335

Cut Wrap Morpher


Tech. Building Blocks for Aero Optimization2 S l S d

• Basic solver enhancementsP b d l d l d

2. Solver Speed

rman

ce

Truck (111 million cells)

– Pressure based coupled solver, pseudo‐transient relaxation, higher‐order term relaxation (HOTR), hybrid flow initialization, non‐iterative transient, hierarchy based hybrid

0 768 1536 2304 3072 3840

Perfro

Number of Processor Cores

13.0.0

14.0.0

AMG, higher‐order numerics improvements

• High performance computing (parallel solver)


nce

F1 140M AMD Magny‐Cours

– ANSYS Fluent is a clear leader in high‐performance computing for external aerodynamics

Li l bili h 3000

Performan

14.0.0

Ideal

• Linear scalability shown on > 3000 processor cores (using wall clock time)

• Parallel file I/O

• Smart and efficient partitioning and load

384 832 1280 1728 2176 2624 3072



• Smart and efficient partitioning and load balancing algorithms

ANSYS Aero Capabilities LeadershipANSYS Fluent 15.0

atures ANSYS Fluent 14.x

GPU for radiation, Fluent–Tgrid integration (serial)

Fluent–Tgrid integration (parallel)HPC: linear scalability to >10,000 cores

ANSYS Fluent 14.0Adjoints, higher order numerics and wall treatment

improvements, GPU for radiation

HPC li l bilit t >3000

Fea g g ( )

HPC: scalability and I/O

HPC: linear scalability to >3000 cores

ANSYS Fluent 13.0Pseudo transient solver, bad mesh robustness, mesh‐morpher‐

optimizer, cut‐cell remesh, wall‐film, viewfactor speedupHPC: file I/O, load balancing

ANSYS Fluent 12.0One‐billion (1E9) cell cases

Anisotropic boundary layer remeshingHPC: Parallel File I/O, Linear scalability to >1000 cores

Fluent 6.2Linear scalability to 256 cores

Fluent 6.3Pressure based coupled solver, polyhedra

HPC: improved file I/O


Linear scalability to 256 cores

Q1 2005 Q4 2007 Q2 2009 Q4 2010 Q4 2011 Q3 2012 2013 Time

Tech. Building Blocks for Aero Optimization3 P M t

Ideal process – “One‐click” response surface

3. Process Management

Ideal process One click response surface

• Supply baseline model

• Identify parameters and ranges

S b it j b bt i f• Submit job – obtain response surface

Ideal process automatically manages

• Model modification and remeshingg

• Distributed solve

• Data collation and reporting

ANSYS WorkBench and DesignXplorer meet needs of the ideal process


Tech. Building Blocks for Aero Optimization 4 D t M t

New problem – The engineer is again a bottleneck – A good bl !

4. Data Management

problem!

When extensive simulations are rapidly run, simulation data is d d t f t t th b i f llproduced at a faster rate than can be meaningfully

understood and used by an engineer

Solution Simulation Process Data ManagementSolution – Simulation Process Data Management

• Data handling and storage

• Meta data extraction

• Post simulation database studies


ANSYS EKM

Case StudyObjective:

A “One Click” process for running an aerodynamics DOE (D i f E i t ) t d ft i iti l t(Design of Experiments) study, after initial setup

Setup baseline modelSetup parametersRun and post process entire DOE matrix automatically without user interventionRun and post‐process entire DOE matrix automatically without user intervention


Shape Exploration & Optimization Work Flow

Setup Baseline CFD modelSetup Baseline CFD model

Set Shape Parameters Using Mesh MorpherSet Shape Parameters Using Mesh Morpher

Set Output Parameter (Drag Coefficient)

Set Output Parameter (Drag Coefficient)

Generate and Run DOE MatrixGenerate and Run DOE Matrix

Generate Response Surface & Perform Goal Driven OptimizationGenerate Response Surface &

Perform Goal Driven Optimization


Perform Goal Driven OptimizationPerform Goal Driven Optimization

Baseline CFD Simulation Setup

Mesh Details• Tetrahedral + prism mesh• 5 prism layers from all surface of vehicle• Total cell count ~ 5.0 M• Same mesh is used in all DOE pointsp

Boundary Condition Setup• Inlet : Velocity inlet (V = 80 mph)• Outlet: Pressure outlet (0 Pa (gage))• T l T & Sid S t• Tunnel Top & Sides : Symmetry• Tunnel Road: No slip wall

Solver Setup• Pressure based coupled solverp• RKE turbulence model• 2nd order discretization schemes

© 2011 ANSYS, Inc. September 22, 201114 2011‐01‐0170

Shape Parameter Definition


Backlight angle Tumble home angle Windshield angle


Mesh Morpher Setup

3 M h h3 Mesh morpher parameters

corresponding to 3 shape factors defined


pfor vehicle


Shape parameter and Morpher Control Point

Range Control point movement per Morpher Control Point

Association Baseline Model

degree angle change

Minimum Value

Maximum Value

Backlight angle

Angular displacement (degree) 69 62.5 75.5

0.3077 (m)Control Point movement (m) 0.0 +2.0 -2.0

Tumble home angle

Angular displacement (degree) 32 24.5 39.5 0.0667 (m)

Control Point movement (m) 0.0 +0.5 -0.5

Windshield

Angular displacement (degree) 62 58 62 0.2500 (m)

Control Point


Windshield angle

Control Point movement (m) 0.0 +1.0 0.0

Correlating Shape Parameters with Control Point Movement

Design of ExperimentsLeveraging ANSYS WorkBench Platform


Design of Experiments

Fluent parameters are exposed to and driven by DesignXplorer (DOE and Optimization application)

• Input Parameters– Backlight angle

– Tumblehome– Tumblehome

– Windshield angle

• Output Parameter– Drag force on the vehicle


Design of Experiment

Design Space is defined by the following bounds

• Backlight angle(BA): ‐2 < BA <2

• Tumblehome (TH): ‐0.5<TH<0.5

• Windshield angel (WA): 0<WA<1• Windshield angel (WA): 0<WA<1

DOE Algorithm• Central Composite Design (CCD)

• Design Type : Face Centered with Enhanced Template

• 29 DOE Points Generated• 29 DOE Points Generated


Design of Experiments

After setup, running the DOE matrix of simulations is aAfter setup, running the DOE matrix of simulations is a “One‐Click” process

Workbench automatically runs simulations for all design points one after the otherp

Post‐processing data is automatically gathered and li d D i X l h fsupplied to DesignXplorer where response surfaces,

etc are automatically generated


Response SurfacesResponse Surface are generated based on Non‐Parametric

Regression Model

2D d 3D f b t d t i li th2D and 3D surfaces can be created to visualize the variation of output parameter within the design space.

Chart between predicted and observed value of output parameter

Goodness of fit shows the accuracy of the response surfaces

For bad goodness of fit one need to refine the DOEs to get a better fitted


response surface

Response Surfaces (3D)


Response Surfaces (2D)

F Vs BA F Vs TH

F Vs WA


Parameter SensitivitySensitivity Charts show that drag force is most sensitive to backlight angle and least to windshield angle, for this case


Pareto Plots


Backlight Angle Tumblehome Angle Windshield Angle Drag

Goal Driven Optimization• Goal driven optimization method works on the relative

importance of user specified conflicting goals to get an optimized set of parameters

• Basic goal considered is that of minimizing drag force

Screening method for optimization

Goal: Minimize drag force

3 best designs suggested


3 best designs suggested by DX

Flow Field Design Space Point: Baseline DesignDesign Space Point:‐‐ Baseline Design (Velocity Contours on x=0)


Flow FieldDesign Space Point: Worst DesignDesign Space Point:‐‐Worst Design(Velocity Contours on x=0)


Flow FieldDesign Space Point: Optimized DesignDesign Space Point:‐‐ Optimized Design(Velocity Contours on x=0)


Shape Comparisons

B li D iBaseline Design

Optimized Design

Worst Design


Shape Comparisons


Summary

2010s is the Decade of Optimization

Four key technologies needed for aerodynamics optimization simulation1. Model creation

2 S l d2. Solver speed

3. Process management

4. Data management

A “One‐Click” process is developed and demonstrated for running the entire DOE matrix after initial setupp


Aerodynamics OptimizationG d Ch ll

A simulation tool that can simulate

Grand Challenges

A simulation tool that can simulate

50 design variants (extensive DOE matrix)

50 million cells (accurate model)50:50:5050 hours total elapsed time (weekend process)

50:50:50


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