for recurdyn (2003.5) - functionbay, inc. · pdf filehic displacement mm ... design...

1

Center of

Center of Innovative Design Optimization Technology

발표 순서

최적설계신기술

적용 예

Probabilistic Optimization (DFSS)

3Center of Innovative Design Optimization Technology

설계비용은 제품원가의 5%이지만

설계가 제품원가에 미치는 영향은 70%

70%20%

5%5%

5% PRODUCTDESIGN

50% MATERIAL

15% LABOR

30% OVERHEAD

COST(%)

INFL

UE

NC

E(%

)

설계의 중요성최적설계신기술


Durability Safety

Vehicle Dynamics NVH

설계요구의 다양성최적설계신기술


설계절차의 자동화

설계조건의 통합화 설계해의 최적화

제품원가 절감

제품성능 향상개발기간 단축

컴퓨팅기술의 발전 최적화기술의 발달다분야통합최적설계

(MDO) 기술

최적설계 신기술최적설계신기술


회전시의 소음

공진

와류

유동장

원심력

압력분포

유동해석 구조해석

소음해석

유동장 및압력분포

압력분포

변형

고유진동수

설계요구: •요구되는 유량과 정압을 발생

•효율최대화

•소음최소화

설계변수: 블레이드의 형상, 두께, 개수

자동차 COOLING FAN의 설계최적설계신기술


Time into design process

Conceptual Preliminary Detailed

Knowledge about design

Design Freedom

AerodynamicsStructuresPropulsionsControlsManufacturingSupportabilityCost

100%

Traditional Design Practice최적설계신기술


Cost

Time into design process

Conceptual Preliminary Detailed

Knowledge about design

Design Freedom

AerodynamicsStructuresPropulsionsControlsManufacturingSupportability

100%

Design Vision최적설계신기술


Design Time

1.하드웨어의 고속화

2.프로그램 성능 향상

1. 프로그램간 인터페이스 자동화

2. 상이한 프로그램간의 데이터 공유

3. 복잡한 설계 절차의 자동화

4. 다분야를 동시에 고려한 설계해의 최적화

Design Time

Design Time

Program Run Time

Program Run Time

Program Run TimeProgram Run Time

사람에 의한 데이터 준비 및 평가

설계 데이터의 변경

Program Run Time

Program Run Time

Program Run Time

Program Run Time

Program Run Time

Program Run Time

Program Run Time

MDO 기술 적용의 효과최적설계신기술

10

• 다분야통합최적설계 (MDO) 기술

연구 및 개발

• MDO기술에 의한 산업제품 설계

• 고급설계인력양성 및 산업체에의

기술이전

• 국제협력을 통한 세계적 수준의

MDO 기술 선도

MDO MDO ResearchResearch

Industrial Industrial ApplicationApplication

Training andTraining andEducation Education

iDOT InternationalInternationalCooperationCooperation

센터 목표최적설계신기술연구센터

11

총괄과제

1.최적화기술 개발(Optimization Technology)

2.기반구조 구축(Computing Infrastructure)

3.통합설계 구현(Integrated Design)

산업 제품

적용 기법

설계

관리 기법

최적화 문제

구성 기법

MDO

방법론근사 최적화

기법

전역 최적화

기법

MDOKernel

MDO 프레임웍

“The Ultimate Design Machine”

통합설계

모듈 분산

컴퓨팅

화모듈최적

DB

CAD

구조해석

유동해석

동역학해석

전자기장해석

사용자

연구과제의 구성 및 유기성최적설계신기술연구센터

12

제1총괄과제

최적화기술 개발

최 동 훈한양대학교

이 병 채한국과학기술원

임 오 강부산대학교

왕 세 명광주과학기술원

이 종 수연세대학교

이 태 희한양대학교

민 승 재한양대학교

제2총괄과제

기반구조 구축

정 재 일한양대학교

이 세 정서울시립대학교

이 재 호서울시립대학교

제3총괄과제

통합설계 구현

박 경 진한양대학교

이 동 호서울대학교

이 관 수한양대학교

하 성 규한양대학교

유 홍 희한양대학교

홍 은 지성공회대학교

김 태 권강남대학교

연구 집단최적설계신기술연구센터


TemplatesTemplates

User InputsUser Inputs

Library DataLibrary Data

VehicleDatabaseVehicle

Database

CrashworthinessLPM, DYNA3DCrashworthinessLPM, DYNA3D

External AerodynamicsGM Program


Heat LoadGM ProgramHeat Load

GM Program

Occupant DynamicsCAL3D


Suspension LoadsADAMS


Other AnalysesOther Analyses

Elastic StructuresODYSSEY, NASTRAN


AutomaticModeler

Parameter

Model

DesignProgram

Automatic Modeler & Analyzers

GM의 경우 (1995년도)최적설계신기술


TemplatesTemplates

User InputsUser Inputs

Library DataLibrary Data

VehicleDatabaseVehicle

Database

CrashworthinessLPM, DYNA3DCrashworthinessLPM, DYNA3D



Solar LoadGM ProgramSolar Load

GM Program





Fuel EconomyGM Program

Fuel EconomyGM Program

Other AnalysesOther Analyses



ResultsDatabaseResults

Database MDO

Parametric

Modeler

Discipline

Modeler

Discipline A

nalysis &Sensitivity C

alculations

Integrated Vehicle Design Analysis : MDO

GM의 경우 (1998년도)최적설계신기술

StructuralAnalysis

(NASTRAN)

DesignRepresentation(Unigraphics)

AerodynamicsInterior

Roominess(Excel)

Fuel Economy

Database(MS Access)

Business(Excel)

MultidisciplinaryDesign

Summary ofResults(Excel)

Architecture Configuration & Parameterization

RockerCenterline

DesignedRocker Section

Exterior Width Shoulder Room Gauges, Areas, Section Sizes, Overall Width

Frontal Area

Bo

dy S

tru

ctu

re M

ass

Fuel Economy,Performance

Value ofRoominess

Net Income

What is an archietecture?

1. Common component shared by products2. Common manufacturing processes used

to build the products

MDO System for Vehicle Architectures on the early design phase in ‘GMGM’2002




Panther Program

Durability Safety

Vehicle Dynamics NVH

Simultaneous Analysis

Improve NVH responseswithout penalties on weight or vehicle handling performancewhile including durability and safety as side constraints

Ford Panther Program (1996년도)최적설계신기술


MDO기술을 이용하여 NVH, 차량동역학, 차체내구성, 차체안전성을 고려하여 동시해석 (Simultaneous Analysis)을 수행

Panther Program

초기 20-25%설계단계에서 제품가격의 60% 결정

CAE검증 후 시작품을 제작으로 설계기간 및 비용 절감

Experimental-based 개발시간의 10%로 Analysis-based 개발 수행

9,100만불의 엔지니어링 비용 절감

자동차 단종시까지 10억불의 자동차 유동비(variable cost) 절감

Ford 자동차의 경우 (1996년도)최적설계신기술

Minimize : Vehicle weight

Subject to :

– NVH Constraints

– Roof Crush Constraints

– Full frontal impact Constraints

– 50% frontal offset impact Constraints

– Side impact Constraints

b

t

DbendingStaticDtorsionStatic

HzfHz

≤≤≤≤ 3.296.26 3

( )( ) ( )lbkNPloadpeakCritical

DancedistCrush

cr 600027"5

≈≥≤

"10≤trusioninboardToe

%1045

450

≤≤

≤

totalPGChest

HIC

mmntDisplacemeCVCriterionViscous

2.2754.0*

≤≤

MSC.NasranGlobal Response SurfaceDOE : Optimul Latin Hyper Cube

RADIOSS codeMADYMO

MDO of Vehicle Structure in ‘FORDFORD’ 2002



Optimization Problem and Method 1

Numerical Results and Discussions 2


발표 순서


적용 예



1

z' z y'

y

1t

2t3t

1SV

θ=2SV

BAR(362), ELAS1(507), QUAD4(2357), ROD(47), DAMP1(4), HEXA(344), CONM2(169), PENTA(92), TRIA3(616)

f) Rockerc) B Pillar

b) A Pillar

a) Front Roof Raile) Side Roof Rail

d) C Pillar

OPTIMIZERUsing TDQA

MSC/NASTRANINTERFACE

MSC/NASTRAN

DesignVariables

Analysis &DSA Data

Input File Output files

EXP-SECT Mode-TrackDesign variables

1st bendingfreq.

Hz

2nd bendingfreq.

1st torsionalfreq.

Frequency Constraints

Minimize Weight

Objective

Section Thicknesses (36)Section Shape Variables (112)

Intermediate Variables

Section Properties (A, I, J)Joint Stiffness (K)

차체구조 최적설계 (1)적용 예


차체구조 최적설계 (2)적용 예

1.51321E-041.37998E+0396.03774E-051.37999E+0310

.

.

.

.

.

.

.

.

.

3.78819E-021.38829E+0324.30770E-021.38883E+031

Max. Constraint ViolationWeight (kg)

IterationNumber

Max. constraint history of vehicle structure

-0.0050.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.050

1 2 3 4 5 6 7 8 9 10

IterationM

ax. C

onst

rain

t Vio

latio

n

t = 1.80 mm t = 1.00 mm

x

y

x

y

Convergence History

Optimization Results Example Shape Change – A Pillar Upper

•Objective convergence history of vehicle structure

•1350

•1360

•1370

•1380

•1390

•1400

•1 •2 •3 •4 •5 •6 •7 •8 •9 •10

•Iteration

•Wei

ght (

kg)

24

전기자동차용 맥퍼슨 현가장치 (1)

Model

Program

Design variables

- Kinematic model of Automotive Suspension

- Analyze Suspension and Steering Characteristics

- ADAMS/Car 11.0 (Mechanical Dynamics, Inc.)

- Coordinates of Hard Points (15)

ADAMS/Car model (Front view)

Constraints

Objective

- The Differences between suspension/steering

analysis response and target value Min.

- Suspension/Steering analysis response ≤ Upper bound

- Suspension/Steering analysis response ≥ Lower bound

적용 예

25


Target

Initial

Wheel travel

Performance

Target

Initial

Wheel travel

Performance

Bound

Initial

Performance

Wheel travel

Bound

Initial

Performance

Wheel travel

Design requirements

Camber Angle

Toe Angle

Anti-Dive

Roll Center Height

Caster Angle

SuspensionCharacteristics

Performance Design variables

적용 예

26


WindowScripting

Host

ADAMS/Car

EMDIOS

실행명령

Performance

GUI를 통한실행명령

Designvariable

적용 예

27


Toe angle

-100 -50 0 50 100

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6 Lower Target Initial Upper FinalSQP

Wheel travel (mm)

Toe

angl

e (D

eg)

Caster angle

-100 -50 0 50 100

1.0

1.5

2.0

2.5

3.0

3.5

4.0 Lower Target Inital Upper FinalSQP

Wheel travel (mm)

Cas

ter a

ngle

(Deg

)

Anti-dive

-100 -50 0 50 100

0

5

10

15

20

25

30 Lower Target Initial Upper FinalSQP

Wheel travel (mm)

Ant

i-div

e (%

)

Roll center height

-100 -50 0 50 100-300

-200

-100

0

100

200

300

400 Lower Target Initial Upper FinalSQP

Wheel travel (mm)

Rol

l cen

ter h

eigh

t (m

m)

Camber angle

-100 -50 0 50 100

-3.0-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.53.03.54.04.5

Lower Target Initial Upper FinalSQP

Wheel travel (mm)

Cam

ber a

ngle

(Deg

)

적용 예

28

Design Requirements

Design Variables

Design Objective

•Wheel travels of six wheels (6)•Equal load sharing among six wheels in steady state (6)

•Track tension (1)•Initial charge pressures of the third and forth HSU’s (2)

•Side Constraints

•Initial charge pressures for the first, second, fifth, and sixth HSU’s (4)

•Static track tension (1)•Length of a gas chamber (1)•Preload on Beleville springs (1)•Inside radius of an orifice (1)•Flow rate at choking point (1)

Minimize the maximum acceleration of the hull of a tracked vehicle when it runs over a semi-circular bump of 0.36m radius with a velocity of 40 km/h

Problem Formulation 적용 예

29

Optimization Result - Acceleration적용 예

30

Optimization Result – Wheel Travel적용 예


Inflow

Tubes

Y

S

P

Outflow

x

y

X

D2

Z

x

z

D1

(a) Top view

(b) Front view

Computational Domain

31.00 mm12.12 mm0.98 mm7.00 mm6.06 mm1.40 mm3.80 mm

X ( width of the channel )Y ( length of the channel )Z ( height of the channel )S ( pin spacing )P ( pin pitch )D1 ( pin diameter at z = Z )D2 ( pin diameter at z = 0 )

valuesParameters

Baseline geometry

Plate-Fin and Tube Heat Exchanger적용 예


Objective

- Minimize the pressure drop, - Maximize the overall heat transfer rate, Nu

p∆

Constraints and Side constraints

23S D X+ ≤2P D Y+ ≤

( )1/ 222

2 / 2D P S ≤ +

2D S≤2D P≤

1 2D D≤

0 10S≤ ≤0 12P≤ ≤

10 8D≤ ≤

20 8D≤ ≤

Side constraints [mm]Constraints

Y

Inflow

Outflow

y

x

S

P

D2

D1

Z

X

x

z

Layout of the heat exchanger

, , , , Design Variables

- Tube spacing (S), Tube pitching (P), - Upper and lower diameters of tube (D1, D2)

Multi-Objective Problem 적용 예


Optimization using Fluent

OPTIMIZER

FLUENTINTERFACE

FLUENT

DesignVariables

ObjectiveFunctions

Input DataD1, D2, S, P

Output Filesp∆ , Nu

적용 예


80 100 120 140 1602

4

6

8

10

12

gfe

dc

b

a

a. (0.8, 0.2)b. (0.7, 0.3)c. (0.6, 0.4)d. (0.5, 0.5)e. (0.4, 0.6)f. (0.3, 0.7)g. (0.2, 0.8)

(ω1 , ω

2 )

F 2(X),

Nu-1

(x10

-2)

F1(X), ∆P [Pa]

Pareto Solutions 적용 예


발표 순서


적용 예


..

Optimization ProblemsOptimization Problems

Deterministic Optimization ProblemDeterministic Optimization Problem

Find deterministic design variablesto

minimizesubject to

[ ]1 2, , , Tnx x x=x L

( )f x( ) 0,jg ≥x

,L Ui i ix x x≤ ≤

1, 2, ,j m= L

1, 2, ,i n= L

Probabilistic Optimization ProblemProbabilistic Optimization Problem

Find random design variablesto

minimizesubject to

[ ]1 2, , , Tnx x x=x L

( )f x( ) 0,jg ≥x

,L Ui i ix x x≤ ≤

1, 2, ,j m= L

1, 2, ,i n= L

Probabilistic OptimizationProbabilistic Optimization

..

Random Variable Random Variable ΧΧ

확률밀도함수

누적확률밀도함수

확률밀도함수확률밀도함수((PDF)PDF)

[ ] ( )( )( )

( ) 1

X

XX

X

P x X x dx f xdF xf x

dx

f x dx∞

−∞

≤ ≤ + =

=

=∫

누적확률분포함수누적확률분포함수((CDF)CDF)

[ ]( )XF x P X x= ≤

21 1( ) exp

22X

XXX

xf x µσπσ

− = −

정규분포:


..

Characterizing Probability Distributions Characterizing Probability Distributions

'1 ( )x Xxf x dxµ µ

∞

−∞= = ∫

( )22 22 ( ) ( )x x x XE x x f x dxσ µ µ µ

∞

−∞= = − = −∫

Mean

Variance

Describe a distribution in terms of

• Average - mean

• Spread - variance

• Symmetry - skewness

• Peakedness - kurtosis

Skewness

Kurtosis

( )3 33 ( ) ( )x x XE x x f x dxµ µ µ

∞

−∞= − = −∫

( )4 44 ( ) ( )x x XE x x f x dxµ µ µ

∞

−∞= − = −∫


..

SixSix--SigmaSigma

For Normal DistributionsFor Normal Distributions

•• 68.3% of values will fall ±1σ around mean

• 95.5% of values will fall ±2σ around mean




..

SixSix--SigmaSigma

3.40.00299.99999986

2330.5799.9999435

62006399.99374

66,8032,70099.733

308,73345,40095.462

697,700317,40068.261

Defects per million(with 1.5σ shift)

Defectsper million

PercentVariation

Sigma ±σ

FormerEngineering

Target

NewPhilosophy

' 1.5µ µ σ= + 1.5 Sigma Shift


Probabilistic Optimization Problems Probabilistic Optimization Problems

Robust Optimization ProblemRobust Optimization Problem

Find

min. s.t.

(1 )f fαµ α σ+ −0,

j jg j gkµ σ+ ≥

Reliability Based Design Reliability Based Design Optimization ProblemOptimization Problem

Find

min. s.t.

,i i i

L Ux x xµ µ µ≤ ≤

( )xf µ[ ( ) 0] ,U

f jP g P≤ ≤x

1 2, , ,

n

T

x x xµ µ µ = xµ L

1, 2, ,j m= L

1,2, ,i n= L1, 2, ,j m= L

1 2, , ,

n

T


Recent RBDO ProblemRecent RBDO Problem

Find

min. s.t.

(1 )f fαµ α σ+ −

[ ( ) 0] ,Uf jP g P≤ ≤x 1, 2, ,j m= L

1 2, , ,

n

T


1,2, ,i n= L,i i i


1, 2, ,i n= L,i i i


2x

1x

1x∆

2x∆

cfδfδ

*cx xδ±

rx xδ∗ ±

Robustness of ObjectiveRobustness of Feasibility


Pfj=P( gj(X) > 0) ≤ Pju, j=1,…,mPfj=P( gj(X) > 0) ≤ Pj

u, j=1,…,m

Minimize

subject to

, 1, 2,...,L Ui i ix x x i n≤ ≤ =

( )f x

Tools Suite

Monte Carlo Simulation

MVFO Method

FORM

, 1, 2,...,L Ui i ix x x i n≤ ≤ =


Formulation for Reliability Based OptimizationFormulation for Reliability Based Optimization

43

Center of


F.E.Model of Automotive Door and Loading andBoundary Conditions

Model

Program

Design variables

- 자동차 프론트 도어의 유한요소모델

- 테일러드 블랭크를 적용하기 위해 각 파트의 두께와 그 분포를 결정

-FEMB/GENESIS 6.0 (VMA)

- 위상 최적설계 : 각 요소의 정규화된 밀도

- 치수 최적설계 및 이산 설계 : 각 파트의 두께

- 형상 최적설계 : 각 파트의 경계 위치

Constraints

Objective- 위상 최적설계 :

- 치수, 형상 최적설계 및 이산 설계

: 자동차 프론트 도어의 질량 최소화

- 위상 최적설계 : 초기 구조물 질량의 30%- 치수, 형상 최적설계 및 이산 설계 :

초기 모델 대비 변형량과 1차 고유 진동수의 성능 향상

임의의 파트 두께는 이웃하는 파트 두께 대비 2배 이하

단, Φ: 구조물의 굴성, ψ: 고유 진동수

첨자 o : 초기 모델

Example of Tailored Blank Door

ΨΨ

+ΦΦ 0

0

Minimize

구조최적설계와 실험계획법을 이용한 자동차 도어 설계적용 예


Optimization results

0.850.650.650.751.3thickness

32222Level

CenterLowUpperRightLeftPart

초기 모델

이산 설계(실험계획법)

형상 최적설계

치수 최적설계

16.3667Kg

16.302Kg

17.0089Kg

16.357Kg

위상 최적설계

Topology optimizationresult with 30% mass

Part selection

Discrete Design

Optimum Distributionof Parts (Shape optimization)

최적 모델

구조최적설계와 실험계획법을 이용한 자동차 도어 설계적용 예


가격 / 신뢰성 우수

에너지 밀도 큼

내 환경성

토오크 최대화토오크 리플

최소화

고 효율 / 고 성능화

설계목적

Switched Reluctance Motor (SRM)SAO 적용 예


Objective

Maximize Average Torque

Constraints

Torque ripple

Maximum Current Phase

( )aveT

( ) %20≤ripT

( ) ( )A6max ≤I

Design Variables

Switching on Angle

Switching off Angle

Rotor Pole Arc

( )onθ

( )offθ

( )rβ

Optimization Problem of SRMSAO 적용 예


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Angle (deg.)

Ave

rage

Tor

que

(N-m

)

75.0

80.0

85.0

90.0

95.0

100.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0Angle (deg.)

Tor

que

Rip

ple

(%)

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0Angle (deg.)

Max

imum

Cur

rent

Pha

se (A

)

0.15

0.16

0.17

0.18

0.19

0.20

0.21

0.22

0.23

0.24

45.0 47.0 49.0 51.0 53.0 55.0 57.0 59.0

Angle (deg.)

Ave

rage

Tor

que

(N-m

)

45.0

55.0

65.0

75.0

85.0

95.0

105.0

45.0 47.0 49.0 51.0 53.0 55.0 57.0 59.0Angle (deg.)

Tor

que

Rip

ple

(%)

4.00

4.05

4.10

4.15

4.20

4.25

4.30

4.35

4.40

45.0 47.0 49.0 51.0 53.0 55.0 57.0 59.0Angle (deg.)

Max

imum

Cur

rent

Pha

se (A

)

Switching on Angle Switching off Angle

Parameter Studies of SRMSAO 적용 예


Convergence History Torque

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6 7 8 9Iteration

Ave

rage

Tor

que

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

0 1 2 3 4 5 6 7 8 9Iteration

Max

imum

Con

stra

int V

iola

tion

0

0.1

0.2

0.3

0.4

0 10 20 30 40 50 60 70 80 90

Rotation Angle (deg.)

Tor

que

(N-m

)

Initial DesignOptimum Design

Initial

Optimum

onθ offθ rβ aveT ripT maxI

22.4 55.7 38.0 0.30 19.98 4.91

25.2 45.1 30.0 0.17 98.60 4.02

Optimization Results of SRMSAO 적용 예

50

Design Framework

- legacy software and commercial tools

- graphical user interface, window-style menu system

Visual Modeling

Software Integration

Automation of the Design Processes

분산객체 (Distributed Object) 시스템의 지원

The design framework is intended to link together disciplinary analyses in a

distributed, heterogeneous computing environment, and automate data

exchange for the purpose of collaborative design

-Java RMI (Remote Method Invocation)

최적화(Optimization) - MDO

설계 프레임웍다분야통합최적설계

51

Design Philosophy

편의성: 지능형 사용자 인터페이스를 통한 편리한 시스템 사용

Script를 이용한 명령(SC), GUI를 이용한 명령(GC),

SC와 GC는 기능적 동치

다양성: MDO의 특징인 분산컴퓨팅 환경과 다양한 플랫폼을 지원

(Java RMI, Agent)

호환성: CAD/CAE 간 이질적 데이터의 호환성 제공을 위한 표준 데이터

양식에 산업체 국제표준 수용 (STEP, XML)

효율성: 소프트웨어 공학적 시스템 개발을 통한 개발 효율 및 사용 효율

추구 , 객체지향 시스템 개발 방법론(UML)

확장성: 새로운 CAD/CAE 또는 데이터 양식을 수용

(Agent, 객체지향 방법론)

확대성: 대규모 및 소규모 MDO 수요를 모두 만족

잘 정의된 API


52

Framework 개발 환경

Java RMI 을 이용한 분산 환경 구현

Visibroker4.0 Java 및 C++

Windows 관련 개발 환경

Microsoft Visual Studio 6.x

Java 관련 개발 환경

JDK1.3

JBuilder3.5,Visual J++: GUI 구현

Javacc: script 구현

Studio.J: 데이터 입출력 및 분석

UML 시스템 설계 환경

Rational Rose 2000

Configuration Management

ClearCase

C++, Fortran 프로그램 지원


53

EMDIOS

EMDIOSEExtendable xtendable MMultidisciplinary ultidisciplinary DDesign esign IIntegrationntegration& & OOptimization ptimization SSystemystem

Resource Wrapping AutomationPost Process & Monitoring

Database

Various Optimizers &MDO Kernel

Process Integration

EMDIO-L Visual Modeling


54

EMDIOS Architecture

DOE Modules

GlobalOptimizer(s)

Driver Resource

Visual Modeling

EMDIO-L Manager

Unknown Resource

PROCESS Manager

EDOM Manager

Optimization Schedule Template Manager

DATABASE Manager

USER INTERFACE

EMDIO-L Parser

CO MDF IDF STANDARD

MDO Kernel

Message Manager

I/O Manager

EDO

M A

bstract Layer

EDO

M A

bstract Layer

LocalOptimizer(s)

Driver Resource

Non Linear Analysis

Crash Analysis

Experimental Results

ApproximationModules


55

Process Integration

해석 시스템간에는 File Wrapper 가 존재하여 각각 해석 시스템에대해서 투명성을 제공( 인터넷과 레거시 환경을 구분하지 않는다).

자바 RMI를 이용하여 이기종간의 해석 시스템을 호출.

RMI (Remote Method Invocation)

인터넷/인트라넷

Client Server

Optimizer CAE S/WeDOM Server (Mac G4 based)

eDOM Server (Win NT/9X)

eDOM Server (UNIX based)


56

Automation

PROGRAM1.EXEFor Suspension



Design Framework provides to organizations a complete solutionfor the automation of the product design process


57

Various Optimizers

DIRECT Method

Genetic Algorithm

Global Optimizer

ADS (Automated Design Synthesis)

DOT (Design Optimization Tools)

NUOPT iDOT (도입예정)

Local Optimizer

Typical Local Approximation Methods

- One-Point Approximation

- Two-Point Approximation

- Multi-Point Approximation

New Two-Point Approximation Method

- Two-Point Diagonal Quadratic Approximation

Gradient Based Approximation

Typical RSM based on Experimental Design

- Quadratic Approximation Modeling

- Alphabetic Optimality Criteria

New RSM based on Experimental Design

- Subspace CCD/SCD

- Progressive Quadratic Approximation

- Augmented D-optimality

Function Based Approximation

Virtual Analysis


Gradient Based Approximation

Typical RSM based on Experimental Design

- Quadratic Approximation Modeling

- Alphabetic Optimality Criteria

New RSM based on Experimental Design

- Subspace CCD/SCD

- Progressive Quadratic Approximation

- Augmented D-optimality

Function Based Approximation

58

EMDIOS Capabilities

MDO MethodsMDO Methods

다양한 MDO 방법론의 도입

More EfficiencyMore EfficiencyFor ModelingFor Modeling

Windows계열의 인터페이스를이용한 최적화 문제 설정

Parallel ProcessingParallel Processing

MDO문제를 병렬로 처리

Distributed Design SystemDistributed Design System

이기종간의 분산환경 지원

EMDIOEMDIO--LanguageLanguage

NASTRAN 문법의 자체언어를 장착


59

Customization

산업체에서 제공하는해석 관련 컴포넌트 EMDIOS의 서브 컴포넌트

iDOT EMDIOS 산업체 Design Framework

iDOT EMDIOS provides iDOT EMDIOS provides Various Optimization Components Various Optimization Components

and GUI Componentsand GUI Components


60

Rear Multi Link Suspension

Model

Program

Design variables

- Rear Multi Link Suspension

- Analyze Suspension and Steering Characteristics

- In-House Program (developed by Hyundai Motors)

- Coordinates of Hard Points (9)

Rear Multi Link Suspension

Constraints

Objective

- The Sum of Linkage forces Min.

- Assist arm ball joint force ≤ Upper bound

- Upper arm ball joint force ≤ Upper bound

Simple Model


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