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Industrial design optimization using open source tools

Paolo Geremia

6th OpenFOAM WorkshopPennState University, USA

13-16 June, 2011

Copyright © 2011 Engys Srl. All rights reserved. Reproduction prohibited.

Contents

• Background

• Optimisation Services

• Example 1: Rudder Shape Optimisation

• Example 2: Bare-hull Optimisation

• Example 3: Catalytic Converter Optimisation

• Conclusions


Background

• 8+ years experience managing industrial CAE projects involving Multi-Disciplinary Optimisation methods

• 10+ years track-record using, developing and supporting FOAM/OPENFOAM® for industry

• Main optimisation platforms → modeFRONTIER (ESTECO)DAKOTA(Sandia OSS)

• More than 30 publications to date on multi-disciplinary optimisation(real industrial applications and proof-of-concept)


Why Optimisation?

• Multi-objective design optimisation techniques are ideal for:

Finding the optimal layout of the design solution

Automating the design process instead of trial-and-error approach

Multi-disciplinary process integration (e.g. CAD+Mesh+FEM+CFD)

Finding the most relevant design parameters affecting the solution

Evaluating the robustness and stability of a solution for a given range of parameters

Better understanding of design space response

Better design with reduction of costs and speed-up of time-to-market


How Traditional Optimisation Works

• Optimisation tools work like software “robot”• For each design evaluation, the optimiser automates the following steps:

Input file(s) creation with updated values of design parameters Batch run of application(s) Reading of results from the output file(s)


Output 1

Output 2

Output M

Input 1

Input 2

Input N

Input File

Output File

Application

Input File Template

Input 2Input 1

Input N

Output File Template

Output 2Output 1

Output M

my_application.exe

Application Batch Script

Optimisation Tool

Contents

• Background





• Conclusions


Optimisation Services

Analysis & Design

SupportDevelopment

Open Source


Optimisation Services

• Coupling with most CAE tools OSS, commercial and in-house tools

CFD, FEM, 1D, Multiphysics, Multibody, Manufacturing process simulation, etc

• Design Of Experiments (DOE)

• Multi-objective constrained optimisation

• Model calibration

• Sensitivity analysis

• Tolerance/Robust design

• Model creation for data analysis, prediction, regression and correlation


CAD

CFD

FEM

Multi-physics

Multi-body

1D

Input 1In

pu

t 2

0 1Output 1

Contents

• Background





• Conclusions


Example 1 | Rudder Shape Optimisation

• US Navy surface combatant ca. 1980 - Model 5415

• Twin open-water propellers driven by shafts supported by struts

• Optimisation problem: minimise forward resistance and maximise lift of 2D rudder hydrofoil


Example 1 | Parametric CAD Model

• 2D cross-section of rudder is considered: NACA0015 hydrofoil / 8°angle of attack

• Parametric CAD: baseline NACA0015 + Bézier parametric curve

• 4 design variables in total (i.e. X1,Y1 and X2,Y2)

• CAD tool used: GMSH


X1,Y1

X2,Y2

Example 1 | Meshing

• 2D cross-section hydrofoil is considered

• Automatic hex mesh applied


Example 1 | CFD Solution and Results

• Single-phase solver

• Resistance and Lift values written to text files for every iteration and read by DAKOTA


U p

Example 1 | Optimisation Strategy

• Goal: finding the best performing design for resistance and lift by varying X,Y coordinates of control points of hydrofoil’s parametric curve

• DAKOTA’s optimization capabilities include a variety of gradient-based and nongradient-based optimization methods.

• A derivative-free Multi-Objective Genetic Algorithm (MOGA) was applied in this case: MOGA allows multiple asynchronous concurrent design

evaluations

MOGA can fully exploit both DAKOTA and OPENFOAM license-free scalability


Example 1 | Optimisation Workflow

SnappyHexMeshGMSH

DAKOTA

OpenFOAM

Design ParametersInput Variables2 Beziér curve control points X and Y coordinates Output VariablesPressure and viscous drag forceLift

Design ObjectivesMinimise resistanceMaximise lift

Optimization Setup Exploration Phase:MOGA algorithm – max no. of iterations: 35Generation size: 20No. of concurrent design evaluations: 4


Example 1 | Optimisation Results


Resistance

Lift

Best ResistanceCompromise

Best Lift

Contents

• Background





• Conclusions


Example 2 | Bare-hull Optimisation

• Model 5415: half bare-hull model considered

• Optimisation problem: minimise ship forward resistance and maximise displacement by changing the shape of the bow


Example 2 | Bare-hull Morphing

• STL surface created by the CAD is read by the morphing tool• Different hull geometries generated by morphing of bow geometric

shape• 3D graphic modeling tool used for Surface morphing: Blender (OSS)


PARAMETRIC SHAPE

Example 2 | Bare-hull Morphing

• Bow shape morphing:

Bulb: translation along Y and Z of 6 independent control points of morphing boxes

Hull: translation along Y of 3 independent control points of morphing boxes


Example 2 | Bare-hull Mesh


• snappyHexMesh:

Block hex + split hex elements

1,5M cells

5 wall extrusion layers

Example 2 | Bare-hull CFD results


Example 2 | Optimisation Workflow

SnappyHexMeshBlender

DAKOTA

OpenFOAM

Design ParametersInput VariablesBulb: translation along Y and Z of 6 independent control pointsHull: translation along Y of 4 independent control pointsOutput VariablesPressure and viscous drag forceDisplacement

Design ObjectivesMinimise resistanceMaximise displacement

Optimization Setup Exploration Phase:LHS sampling DOE algorithm –No. of samples: 100MOGA algorithm using Neural Network response surface


Example 2 | Response Surface Creation

• Problem: computational time required by CFD simulations is very expensive for a massive optimisation run

• Solution: CFD runs are replaced by response surface surrogate models created for resistance and displacement outputs

• The 100 initial CFD samples are used for training of response surfaces• Neural Network model applied• MOGA optimisation using response surface models is carried out• Overall CPU time to run the optimisation phase: 2 mins!


YBfront

ZBfr

on

t

MIN MAXResponse surface - Displacement

Example 2 | Bare-hull Optimal Shape


• Pareto front (i.e. the set of the optimal solutions): 3 different groups of optimal solutions

• Best for resistance



• Pareto front: 3 different groups of optimal solutions

• Compromise for resistance and displacement



• Pareto front: 3 different groups of optimal solutions

• Best for displacement

Contents

• Background





• Conclusions


Example 3 | Catalytic Converter Optimisation

• CAD geometrical shape parameterisation

• X,Y position of 4 cross-section points

12

34

Different shapes generated



• Catalytic converter inlet duct shape optimisation


SnappyHexMeshCAD

DAKOTA

OpenFOAM

Design ParametersInput VariablesX and Y coordinates of 4 cross-section pointsOutput VariablesFlow uniformityPressure drop

Design ObjectivesMaximise flow uniformityMinimise pressure drop

Optimization Setup Exploration Phase:Surrogate-based global MOGA algorithm – max no. of iterations: 10Generation size: 32



BaselineOptimised

Contents

• Background





• Conclusions


Conclusions

• The coupling between OSS DAKOTA and OPENFOAM was done successfully.

• Different shape parameterisation techniques were evaluated.

• DAKOTA capabilities were efficiently exploited for different naval and marine applications.

• Benefits of DAKOTA and OPENFOAM scalability are huge for product development speed-up and reduction in costs.


Disclaimer


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OPENFOAM® and OpenCFD® are registered trade marks of OpenCFD Ltd.None of Engys offerings are approved or endorsed by OpenCFD Limited, the producer of

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Download - Industrial design optimization using open source tools · Industrial design optimization using open source tools ... Optimisation Workflow GMSH SnappyHexMesh DAKOTA ... translation

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