multidiscippyplinary optimization for industrial ... · pdf filemultidiscippyplinary...

Multidisciplinary Optimization p y pfor Industrial Aeronautical Applications

M Delanaye F Lani I LepotM. Delanaye, F. Lani, I. Lepot

Dr. Michel DelanayeGeneral Manager

Contact: michel delanaye@cenaero be

Teratec Forum 2010 – 16/06/2010 © 2010 Cenaero – All rights reserved

Contact: [email protected]

Aim & Outline

• Aim– Present some “real” applications of multi-objective– Present some real applications of multi-objective

multidisciplinary optimization for complex design problems in aeronautics

• Outline– Quick presentation of Minamo platform

E l 1 d i f t t ti t– Example 1: design of counter rotating open rotors (aeroacoustics)

– Example 2: design of composite structural parts– Example 2: design of composite structural parts (cost, manufacturing)


Who we are ?


R&T private company focused on simulation and modelling

process modelingprocess modeling

integrity analysis

structure design

On demand

flow analysis

Multidisciplinaryanalysis

d d


aerodynamic design

Minamo basics


Surrogate assisted Genetic Algorithm workflow

UserUserSpecifications

Gradients for complex objectives usually non available !

Specifications

Approximate ModelANN RBFANN, RBF, …

OptimizationOptimizationppGA, SA, … DATABASEDATABASE

Accurate Model Accurate Model CFD / Structure / Exp. / ...

Performance CheckEND


Surrogate based optimization

ectiv

e

Actual Optimum

Approximate

Obj

e

PredictedOptimum

p

Approximate model

Initial Accurate Results

Design Variable


Surrogate based optimization

PredictedOptimum

Actual Optimum Artificial Neural Networksec

tive

OptimumRadial Basis FunctionsKriging

Obj

e

Appro imate

...

Approximate model

Initial Accurate Results

Design Variable


Key issues

• Efficient design exploration techniques– Reduce the number of samplings (high fidelity– Reduce the number of samplings (high fidelity

simulation)

• Accuracy of response surfaces (models)

• Methodologies to assess the quality and reliability of the modelsy

• Efficient multi-objective GAEfficient multi objective GA

• …


…

Derivative free optimization with Minamo

Illustration of adaptive sampling capability


Efficient Design Exploration

Candle functionGlobal optimum

Misplaced optimum

Exact solution

Standard LHSsampling

Auto‐adaptive LCVT sampling


Monitoring/ReliabilityLeave-One-Out (Open gap/Stall point)

DOE

Optimization

Large DoE scatter - Stabilization afterLOO Reliability Assessment:

Large DoE scatter Stabilization after about 50 design iterations: 2 different promising design families pointed out, satisfying the manufacturing constraints

Isentropic efficiency correlation coefficient

0.915502 (DoE) 0.9685 (optimization)


satisfying the manufacturing constraints

ANOVA – Sobol indices

First order sensitivities and interaction volume (if required higher order sensitivities) quantification

0 3

Relative Importance of Parameters

sensitivities) quantification

0,2

0,25

0,3

s

Section 5 first camber parameter

0,1

0,15

Sobo

l Indi

ces

Isent_Eff_Large_Gap_1.10Isent_Eff_Small_Gap_1.10Isent_Eff_Large_Gap_1.13Isent_Eff_Small_Gap_1.13

p

0

0,05

Illustration on NEWAC optimization accounting for engine wear


Illustration on NEWAC optimization accounting for engine wear

Direct CAD access CATIA – CAPRI – mesher

Master script (Python or C++, called by Minamo)p ( y , y )

D M

odel

mes

h

difie

d m

odel

Mod

ified

D M

odel

esh

ef. m

esh

CAPRI client

CA

Ref

.

Mod

CAD

GeomSim Discrete, MeshSim

M CA M

e

Re

Linux/Windows

SOAP IGG/AutoGrid 5/Samcef/Abaqus CAE, etc

TCP/IP

Windows/Linux

CAPRI server

CATIA V5 – SolidWorks – UG NX – Pro/Engineer ‐ OpenCASCADE …


Summary of Minamo Features

• Mono- and multi-objective evolutionary algorithms, including memetic approaches

• Online surrogates approach• Online surrogates approach • Space fill and auto-adaptive DoE (LHS, CVT,

LCVT, …)• Efficient adaptive nonlinear global and local• Efficient adaptive nonlinear global and local

models (ANN, RBFN, Kriging, SVM, …)• Response surfaces reliability through leave-k-out

cross validationcross-validation• Constraints activity interactive monitoring• Quantitative Variance Analysis tool (ANOVA):

Sobol sensitivity indices estimation• Data mining capabilities for efficient multi-criteria

decision making (self organizing maps, …)• Simulation coupling through Python scripting• Native and neutral CAD access• Available in standalone version or as engine


gplug-in in other software (Optimus)

Example 1: Design of Counter-Rotating Open Rotors


Aeromechanical Optimization of a Counter-Rotating Open Rotor (CROR)

• Open Rotor Advanced Concept Studies

• Potential to meet drastic fuel burn decrease for future passengersdecrease for future passengers aircraft at horizon > 2020

• Challenges:• Noise mitigation while ensuring g g

high efficiency

Teratec Forum 2010 – 16/06/2010 © 2010 Cenaero – All rights reserved 1717

Optimization Specification

• Objective function:• Maximization of global efficiency @ TOC• Minimization of acoustic level @ TO

• Constraints:• Aerodynamic constraints:

• Thrust requirements @ TOC and TO• Torque split @ TOC and TO (DD architecture)• Streamlines contraction/R1 tip vortex

• Mechanical constraints• Max VM stress/ linear FE, rig-scale• Simplified flutter criterion

• Geometric constraints/feasibilitiy: • LE/TE thickness, max thickness, reverse mode,• LE curvature change criterion, curvilinear length of TE, CG position

RPM RPM• RPMR1= RPMR2• Variable blade re-staggering (including between both OPs)• Clipping of rotor 2 is fixed, no contouring modification


pp g , g• Failed or unstable simulations handled by a success switch

In-house Cenaero blade modeler

• Profile shape (6 equidistant sections along blade height)• Maximum thickness• Maximum thickness position• Chord lengthChord length• Stagger angle• Skeleton angle at LE/TE

St ki• Stacking (6 equidistant sections along blade height + 2 additional sections at 90 and 95%)

• CG axial position • CG tangential position

• Blade pitch anglesi l di i bl d lt t iincluding variable delta restaggering between TOC and TO

102 parameters conception space


102 parameters conception space

CROR Optimization Chain Setup

Maximize efficiency @TOCMinimize acoustics @TOAero /Mech constraintsAero./Mech. constraints

Approximate ModelsAuto adaptive RBF

Design of Auto-adaptive RBF

networks

E l ti MO O ti i ti

Experiments

Evolutionary MO OptimizationDATABASE

l A RANSMinamo elsA RANS FE SAMCEF

Post treatment utilities

Minamo

Performance check

ONLINE modelingInfill criteria


Success switchEND

CROR CFD Setup

• elsA (ONERA) RANS simulations with mixing plane• Absolute velocity formulation

N fl ti f fi ld BC• Non reflecting farfield BCs• k-ω Wilcox turbulence model• Jameson's scheme• Full Multigrid (2 levels)

• Mesh convergenceMesh convergence analysis (~ 3 106 nodes)• Cost functions convergence• Farfield boundaries positioning• Regeneration robustness

assesssed through dedicated DoEassesssed through dedicated DoE


Lessons Learned Phase 2Acoustic cost function behaviour

A ti it i bAcoustic criterion may be seen as “minimization of R1 wake, up to the height of R2”

REFERENCE OPTIMIZEDŵ = projection of relative speed fluctuation on the normal to the mean flow

R2”

Wake of R1 reduced up toWake of R1 reduced up to the height of R2

R1 tip vortex strengthenedR1 tip vortex strengthened, but above R2 tip


Pareto front (MM+CFD): acoustic cost function @TO vs global efficiency @TOC – Phase 3

V 1.2

Phase 2

V 2.0


Pareto front: Acoustic cost function @TO vs global efficiency @TOC g y @

V 1.2

Phase 2 Gain: >10 dB

V 2.0

Phase 2A

coevel

Gain: >10 dBEffi: > 1.5 %

oustic cos

AeromechanicalOptimization Phase 3

Mechanical constraints most stringentA

cous

tic l

st functio

p

102 dimensions~ 500 function calls

Aon @

TO


Global efficiency @ TOCGlobal efficiciency

Promising case (selected V2.2)Rotor 1 SS Pressure Distribution

OPTIMIZED REFERENCEOPTIMIZED REFERENCE

Non dimensional static pressure


Example 2: Design of composite structures


Structural preliminary design and optimization of composite structures

1. Objectives– Multi-disciplinary / multi-physics technical objectives:

• Maximize the stiffness• Minimize the mass• Maximize the buckling resistance• Minimize the max / average shear angle of plies due to draping onto complex

areas• Minimize drag (Fluid-structure interaction and CFD computations))•• …

– Economical objective:• Minimize the manufacturing cost (provided a realistic cost model)



1. Constraints– Design rules– Manufacturing constraints– Maximum allowable mass– Damage criterion & modelsg– Technological (eg: problematic vibration, water ingress, flame retardant,

buckling, …) and geometrical constraints

2. Design space– Materials

• Constituents of the plies (fibres and matrix)• Constituents of the plies (fibres and matrix) • Ply thickness and orientation• Stacking sequence• Draping seed point curve splits orderDraping seed point, curve, splits, order

– Geometry• Position, angles, dimensions• Addition or removal of elements (quasi topological approach)Constraints


• Addition or removal of elements (quasi topological approach)Constraints


• Multi-objective, multi-constraint optimisation with a discrete & continuous variables– appropriate & efficient genetic algorithm enhanced with meta-

models (neural network, radial basis functions) for accelerated convergenceconvergence

• Direct access to CAD required for shape optimisation • CAD – Mesh – BCs – Materials associativityy• Materials database with appropriate formalism• Finite element solver with appropriate features (multi-layer shell

elements, failure criteria, …)• Other solvers for the technical objectives• Dedicated cost model introducing manufacturing constraints• In-house libraries & software interfaces


Typical optimization workflows for composite structures

• Minamo – Catia CPD – Simulayt CompositeMinamo Catia CPD Simulayt Composite Link – Abaqus Link in Catia – Simulayt Composite Modeler – Abaqus – Seer DFMp q

• Minamo – Catia – FMS-FMD – CAPRI –vel Minamo Catia FMS FMD CAPRI

Simmetrix – CADMesh – Mecamesh –Samcef (also Samcef Gateway) – Seer DFMat

ion Lev

( y)

• Minamo – Catia – FMS-FMD – CAPRI –Autom

a

Simmetrix – CADMesh – MSC Nastran –Seer DFM


Design and optimization of a satellites dispenser

Initial metallic design design violated constraints => composite


Initial metallic design design violated constraints => composite


2 Objectives:• minimum massminimum mass• minimization of the frequency margin of the fi t l t l ib tifirst lateral vibration mode

352 Constraints:• Maximum allowed cost• Static dynamic• Static, dynamic• Buckling

Design space: laminates & geometry(25 design variables


reduced to 13 after ANOVA)

Optimization Loop Based on Samcef

Minamo.dat

MinamoTMopti.prjDetailed view:

cost.out response.out

Client (LINUX)

Serveur (Windows)

parameter_modification.pyScript Python

model.stlcost.out

Client (LINUX)

Serveur (Windows)

parameter_modification.pyScript Python

model.stl

- client‐server ops

- I/O operationsSEER

geometry_modification.ccGeometry_modification.ccCatia V5

model_reconstruction.pyScript Python

Serveur (Windows)

Client (LINUX) Catia V5

model_reconstruction.pyScript Python

Serveur (Windows)

Client (LINUX)

CADMeshPackagepost.cc

- meshing

- Functioning of in‐h lib i

SEERDFM

Simmetrix

mesh_translationmesh.dat

pli.datSimmetrix

mesh_translationmesh.dat

house libraries

...!parameter definitionINPUT “pli dat”

model.dattsai.txt mass.dat

...

INPUT “pli dat”

model.dat

disp.txt

mass.dat

Samcef V12.1-3

INPUT “pli.dat”INPUT “mesh.dat”

...model.res

INPUT “pli.dat”INPUT “mesh.dat”

!end of the definition ...



1. A slight increase of Target mass

2. 7% improvement of the margin on thethe margin on the frequency

3. Cost reduced by 12%


y

Sensitivity of structural integrity to draping

• Nacelle subjected to pressure and with clamping conditions at the interface with theclamping conditions at the interface with the pylon

• Quasi-isotropic CFRP laminates with 10 pliesQuasi isotropic CFRP laminates with 10 plies

• Design space:• Design space:– Seed point for plies 1-5

• SM = static margin basedon the Tsaï Hill (th) criterionon the Tsaï-Hill (th) criterion



Computational chainMinamo + Catia CPD –Simulayt Composite Link –Abaqus Link in Catia –Simulayt Composite

Flatpattern

Modeler – Abaqus

Shear



Variable: Seed PointVariable: Seed Point

Ply 1 Ply 2 Ply 3 Ply 4 Ply 5

SM Region 1 12% 2% 2% 0% 4%

SM Region 2 8% 1% 9% 3% 3%

SM Region 3 2% 12% 6% 2% 2%

SM Region 4 1% 7% 0% 1% 5%

SM Region 5 8% 2% 0% 1% 1%

SM R i 6 7% 1% 14% 1% 2%SM Region 6 7% 1% 14% 1% 2%

SM Region 7 6% 23% 2% 22% 7%

SM Region 8 3% 0% 1% 8% 4% Coupled optimization of SM Region 8 3% 0% 1% 8% 4% Coup ed opt at o oplies orientations and manufacturing (automatedfiber placement

Orientation of plies + draping seed points position influence the behavior of the structural integraty


fiber placement.influence the behavior of the structural integraty.

Ongoing developments

Sampling and meta-modelingFurther development of auto adaptive samplingFurther development of auto-adaptive samplingKriging + Expected Improvement CriterionSurrogate models coupling: local/global - weighted averageRBFN (auto-)adaptive fine tuningSupport Vector Machines

Optimization - Hybridization:Memetic approaches / efficient global-local couplingExploitation of collective knowledge with multi parent crossoversExploitation of collective knowledge with multi-parent crossovers (UNDX).Gradient knowledge (SPSA, FDSA, …) to be incorporated in genetic

t di t b d t tioperators, e.g. gradient-based mutation.


Conclusion

• Multiobjective, multidisciplinary optimization for complex design problems is not a dream ! Butcomplex design problems is not a dream ! But requires:

– Efficient and powerful optimization engines– Careful accuracy, steering and reliability ad-hocCareful accuracy, steering and reliability ad hoc

methodologies– Fully automated CAD to objective functions

icomputations– Enough computing power

Thank you for your attention


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