MULTIOBJECTIVE OPTIMIZATION OF AUTOMOTIVE VEHICLE GAGE PANEL - - PowerPoint PPT Presentation

multiobjective optimization of automotive vehicle gage
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MULTIOBJECTIVE OPTIMIZATION OF AUTOMOTIVE VEHICLE GAGE PANEL - - PowerPoint PPT Presentation

International Conference on Vibration Problems 2011, September 5-8, 2011, Prague, Czech Republic MULTIOBJECTIVE OPTIMIZATION OF AUTOMOTIVE VEHICLE GAGE PANEL Anatolijs Melnikovs* anatolijs.melnikovs@inbox.lv Alexander Janushevskis* Janis


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SLIDE 1

International Conference on Vibration Problems – 2011, September 5-8, 2011, Prague, Czech Republic

Anatolijs Melnikovs* anatolijs.melnikovs@inbox.lv Alexander Janushevskis* Janis Auzins* Anita Gerina-Ancane** Janis Viba**

*RTU - Riga Technical University Machine and Mechanism Dynamics Research Laboratory M M D Z P L

6, Ezermalas, : +371 67089396 Riga, LV-1006 Fax: +371 67089746 Latvia

www.mmd.rtu.lv/zpla.htm janush@latnet.lv ** Riga Technical University, Institute of Mechanics

M I

MULTIOBJECTIVE OPTIMIZATION OF AUTOMOTIVE VEHICLE GAGE PANEL

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SLIDE 2

INTRODUCTION

Frontal view of gage panels

  • f AMOPLANT vehicles:

*Pictures from AMOPLANT Ltd page: http://www.amoplant.lv

1) 2) 3) RIGA OLD TOWN* *

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SLIDE 3

Manufacturing requirements:

  • Vibrostability of gage panel (Frequency range 10

– 250 Hz; acceleration level 50 m/s2)

  • Gage panel shock resistance (acceleration level

100 m/s2)

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SLIDE 4

3D GEOMETRICAL MODEL OF INITIAL DESIGN OF GAGE PANEL (GP)

The 3D geometrical model consists of 18 parts:

6 deformable bodies and 12 rigid bodies

Calculated inertial properties of GP:

Mass = 1.024189 [kg] Volume = 0.000778 [m3] Surface area = 0.730695 [m2] Center of mass: [ m ] X = 0.053455 Y = 0.031768 Z = -0.036300 Principal axes of inertia and principal moments of inertia: [kg] *[m2] Ix = (0.999996, -0.002695, 0.000849) Px = 0.003885 Iy = (-0.000739, -0.539488, -0.841993) Py = 0.011049 Iz = (0.002727, 0.841989, -0.539488) Pz = 0.013953

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SLIDE 5

FINITE ELEMENT MODEL OF GAGE PANEL

The FE mesh: curvature based mesh

max elements size = 9 mm, min element size = 1.8 mm, element size growth ratio =1.5

GP materials: >ABS 2020 plastic >Alloy steel Model meshed with second-order tetrahedral elements Parabolic solid element STATIC ANALYSIS.The FE mesh consists of ~ 210,000 nodes, ~147,000 elements, ~630,000 DOF

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SLIDE 6

STATIC ANALYSIS OF GP: MAXIMAL STRESSES FROM IMPACT LOAD

Von Mises stresses distribution due to vertical acceleration GP most loaded parts (von Mises stress > 0.9 [MPa] a = 100 m/s^2

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SLIDE 7

STATIC ANALYSIS OF GP: DISPLACEMENTS and STRAINS FROM IMPACT LOAD a = 100 m/s^2 GP displacements from vertical acceleration GP strains from vertical acceleration

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SLIDE 8

FREQUENCY ANALYSIS OF GP OF INITIAL DESIGN

Mode No. Freq (Hertz)

1

99,393

2

150,81

3

222,08

4

230,9

5

264,6

6

317,71

7

363,23

8

395,18

9

446,07

10

478,14

11

529,14

12

560,59

13

591,11

14

608,39

15

640,98

Directional Mass Participation

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SLIDE 9

FREQUENCY ANALYSIS OF GP. MODES ANIMATION

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SLIDE 10

> Modal damping ratio is assumed 0.03

HARMONIC ANALYSIS OF GP

f(t) = A sin (ω t + α) > Frequency range 10<ω<700 [Hz] > Multi-Degree-of-Freedom system: > Total DOF = 392 000

The peak steady state response of GP due to base excitations:

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SLIDE 11

Amplitudes of vertical displacements at the defined points of GP

HARMONIC ANALYSIS OF GP

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SLIDE 12

TIME HISTORY ANALYSIS OF GP

Polyharmonic load on GP: Transient behavior of GP at characteristic points:

Von Mises stress at the center of GP bracket Velocity of the GP at the center point Displacement of the GP at the center point

+ ⋅ + + ⋅ = )) cos( ) sin( ( ) (

2 2 1 1

β α t w A t w A C t f

i

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SLIDE 13

RANDOM VIBRATIONS OF GP

Statistical loads on GP:

PSD of von Mises stress at the center of GP bracket Acceleration power spectral density (PSD)

> The frequency range 10< ω<500 [Hz] > 15 lower modes are taken to account

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SLIDE 14

RANDOM VIBRATIONS OF GP

PSD of vertical displacements at the defined points of GP

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SLIDE 15

RANDOM VIBRATIONS OF GP

PSD of vertical acceleration at the defined points of GP

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SLIDE 16

SUSTAINABILITY ANALYSIS OF GP

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SLIDE 17

MULTIOBJECTIVE OPTIMIZATION OF GP BRACKET

Geometry modeling by SolidWorks (SW) Design of Experiments by EDAOpt¹ Initial GP design 1.Design variables 2.Constraints FEM calculations by SW Simulation

  • 1. Static analysis – impact loads
  • 2. Dynamic analysis:

Harmony analysis Time history analysis

  • 3. Frequency analysis
  • 4. Sustainability analysis

Approximation,

  • ptimization by EDAOpt¹

Responses

  • 1. Optimal shape

geometry by SW

  • 2. Checking results of
  • ptimization by SW

Simulation

EDAOpt¹ - software for design of experiments, approximation and optimization developed in RTU

Optimal design variables

T k x

x F x F x F x F )] ( ),..., ( ), ( [ ) ( min

2 1

=

( )

≤ x g j

( )

= x hl

n

E x∈

The problem is stated as follows: subject to , j=1, 2,…, m, and , l=1, 2, …, e, where k is number of objective functions Fi; m is the number of inequality constraints; e is the number of equality constraints and is a vector of n design variables.

Random vibrations analysis

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SLIDE 18

METHOD USED TO DEFINE CROSS SECTION SHAPE

1) Cross-section shape definition with B-spline knot points 2) 3D- shape creation through path curve Design parameters are NURBS knot points Ranges of Design Parameters: 3) Shape of the bracket (The same on the left bracket)

SHAPE DEFINITION OF BRACKET: 3 ≤ X 1 ≤ 9; 1.5 ≤ X 2 ≤ 6; 1.5 ≤ X 3 ≤ 4

2 1 3

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SLIDE 19

The LH design of experiment is calculated with MSD (mean-square distance) criterion for 3 factors and 40 trial points

THE DESIGN OF EXPERIMENT

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SLIDE 20

Second order local polynomial approximation: Weighted Least Squares Method

∈ ∧

− × − =

X

N j j j j

x y y x x w

2

)) ( ( ) ( min arg

β

β

Approximation quality estimation with crossvalidation error coefficient

2 1 1 2

) ( 1 1 ) ) ( ( 1 % 100 y y n y x y n

i n i n i i i i Xrel

− − − =

∑ ∑

= = − ∧

σ

ε β β β β

∑ ∑ ∑ ∑

= = − = + = ∧

+ + + + =

d i i ij d i d i d i j j i ij i i

x x x x y

1 2 1 1 1 1

Gauss weight function

const u u w = − = ) ) ( 5 . exp( ) (

2

α α

APPROXIMATION OF RESPONSES

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SLIDE 21

Constraints:

] X[ 2 ) ( ) ( ) (

2 3 1 2 3 2 2 2 1

MPa

vonMises

< − + − + − = σ σ σ σ σ σ σ 1) On maximal equivalent stresses in the GP brackets at the defined cross -section points: Cross -section check points: Y1;Y2;Y3;Y4;Y5 < X [MPa]

2) On GP eigenfrequencies Objective functions: 1) v -Volume of the GP 2) Displacements at check points 3) Parameters of environmental pollution MULTIOBJECTIVE OPTIMIZATION OF GP BRACKET

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SLIDE 22

SHAPE OPTIMIZATION OF GP BRACKETS

Constraints: A)Y1;Y2;Y3;Y4;Y5 < 1.4; [MPa] B) Y1 < 1.2 [MPa] and all constraints active Objective functions: 1) v -Volume of the disk 2) max displacements at characteristic points 3) Parameters of environmental pollution

A) B)

Volume = 166880 [mm3] Volume = 167151 [mm3]

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SLIDE 23

OPTIMIZATION OF GP BRACKET

Cross – sections of criterions surfaces and active constraints for variant B

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SLIDE 24

RESULTS OF SHAPE OPTIMIZATION OF GP BRACKET

Initial design of GP Optimized design of GP

Von Mises stress distribution in considered cross-section

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SLIDE 25

Mode No. Freq (Hertz) Initial New

1

99,393 122,65

2

150,81 166,47

3

222,08 233,24

4

230,9 254,51

5

264,6 270,28

6

317,71 367,86

7

363,23 409,5

8

395,18 418,51

9

446,07 488,53

10

478,14 516,69

FREQUENCY ANALYSIS COMPARISON

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SLIDE 26

Initial design

HARMONIC ANALYSIS COMPARISON

Vertical displacements at the defined check points of GP

New design

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SLIDE 27
  • The first results of simulation and optimization of the

vehicle GP are presented

  • The smooth easy technologically realizable shapes are
  • btained by current approach
  • The jagged forms are excluded from optimization process

and there's no need for the excessive computational resources

  • The most time consuming step of the current approach is

implementation

  • f

computer experiments with FEM analysis for building of metamodels of the GP responses. Then solution of several single objective problems and realization

  • f

different aggregation strategies for multiobjective optimization are relatively easy, to obtain an acceptable final solution

CONCLUSIONS

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SLIDE 28

Thanks for your attention!