Multilevel Distributed Structure Optimization Jorg Entzinger Roberto Spallino Wout Ruijter.

Multilevel Distributed

Structure Optimization

Jorg EntzingerRoberto Spallino

Wout Ruijter

2

Outline

• Introduction• Problem description• Program design• Tests and test results• Conclusion

IntroProblem

descriptionProgram design

Tests & results

Conclusion

3

Problem Formulation

Develop a design tool tominimize the weight

of an aircraft substructure subjected to static loadcases.

New design features must be analyzedin an autonomous, overnight run.

IntroProblem


Tests & results

Conclusion

4

Vertical Tail Plane .

IntroProblem


Tests & results

Conclusion

5

Vertical Tail Plane .

IntroProblem


Tests & results

Conclusion

6

Spar Panel Configurations

IntroProblem


Tests & results

Conclusion

7

Finite Element Models

• Linear static analyses– buckling multiplier– maximum strain

• FEM models are parametric

• About 8000 nodes quadratic 3D shell (48000 DOF)

IntroProblem


Tests & results

Conclusion

8

Optimization Problem

Optimize Variables Loads Constraints

VTP

Center Box

Configuration

Stiffener height

Hole position

Stiffener position

Panel thickness

Etc.

Manoeuvre

Rudder

Crash

Maintenance

Assembly

Feasibility- Strain- Buckling

IntroProblem


Tests & results

Conclusion

9

Multilevel Implementation

Level Variables Loads Constraints

Structure - Manoeuvre

Rudder

Crash

Maintenance

Assembly

Component Configuration

Stringer height

Hole position

Stringer position

Panel thickness

Etc.

Shear

Bending

Compression

Strain

Buckling

IntroProblem


Tests & results

Conclusion

10

Structure Level Optimization

Initialize structure

Calculate component loadings and BCs

Converged?

Optimize component 1

Optimize component N

......

Postprocess

IntroProblem


Tests & results

Conclusion

11

Component Level Optimization

Population

FE solver

Selection

Crossover

Mutation

Converged? Optimum

Set of possible solutions

Calculation of pseudo objective (objective + penalties)

Ranking based on pseudo objective

Interchange of parameter values

Random change of param. values

IntroProblem


Tests & results

Conclusion

12


Population

Selection

Crossover

Mutation

Converged? Optimum

FE solver

IntroProblem


Tests & results

Conclusion

13


Population

Selection

Crossover

Mutation

Converged? Optimum

Training data setNeural Networks

IntroProblem


Tests & results

Conclusion

FE solver

14


Population

Selection

Crossover

Mutation

Converged? Optimum

FE solverTraining data setNeural Networks

Accuracy check (FE)

IntroProblem


Tests & results

Conclusion

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Algorithm Overview

• Finite Element Models (Analysis) • Neural Networks (Response Surface)• Genetic Algorithm (Optimization)• Distributed Computing (for Speeding up)

IntroProblem


Tests & results

Conclusion

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Algorithm Features

• Accuracy because of Network retraining• Robustness by the Genetic Algorithm• FE knowledge is preserved in the Neural Network• Neural Networks can be pre-trained offline• Fast optimization• Applicable in an industrial environment

IntroProblem


Tests & results

Conclusion

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Tests

• Box test • Convergence tests• Tests with series of Spar Panels• Half VTP tests • Full VTP tests

IntroProblem


Tests & results

Conclusion

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Convergence

IntroProblem


Tests & results

Conclusion

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Neural Network Accuracy

IntroProblem


Tests & results

Conclusion

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Spar Optimization

• Series of spar panels• Multiple runs with different design considerations

– Different laminate stackings– Different hole placement throughout the structure– Different variables (such as variable stiffener height)– New configurations

IntroProblem


Tests & results

Conclusion

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Spar Panel Series Test

• 36 Components• No access holes demanded in the

6 lowest panels (for both front and rear spar)

• Combined shear & bending loads• Realistic loadcases

IntroProblem


Tests & results

Conclusion

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Spar Panel Series Test• 36 Components• No access holes demanded in the 6 lowest

panels (for both front and rear spar)• Combined shear & bending loads• Realistic loadcases

• 7 HP-UX workstations @400 MHz• Runtime: ca. 18 hours

IntroProblem


Tests & results

Conclusion

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Front Spar Panels

IntroProblem


Tests & results

Conclusion

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Front Spar Panels

• Many stiffeners in lower spar panels (to prevent buckling)

IntroProblem


Tests & results

Conclusion

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Front Spar Panels


• Holes found where not demanded

IntroProblem


Tests & results

Conclusion

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Front Spar Panels


• Holes found where not demanded

• More stiffeners in upper spar panels might be beneficial

IntroProblem


Tests & results

Conclusion

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Rear Spar Panels

IntroProblem


Tests & results

Conclusion

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Rear Spar Panels

• More longitudinal stiffeners might me beneficial (compare with front spar!)

• Conclusion:

add configurations

IntroProblem


Tests & results

Conclusion

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Full VTP Test

• 90 components• Non-realistic global loadcase• Limited set of configurations• No holes required in upper 4 panels

IntroProblem


Tests & results

Conclusion

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Full VTP Test

• 90 components• Non-realistic global loadcase• Limited set of configurations• No holes required in upper 4 panels

IntroProblem


Tests & results

Conclusion

• 27 Win-XP PCs @ 2.6GHz• 3 structure iterations• Runtime: ca. 9 hours.

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Conclusions

• Powerful tool to evaluate the potential of a design

• Flexible in component optimization

• Tests show good optimization results

• Overnight runs possible with sufficient computers

IntroProblem


Tests & results

Conclusion

32

Prospects

• Handle constraints on structure level• Apply for other (aircraft) structures• Enable interaction with other calculations (Flutter)• Apply in other fields (acoustics, dynamics)

IntroProblem


Tests & results

Conclusion

Questions?

Jorg EntzingerRoberto Spallino

Wout Ruijter

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Spar Panel Parametrization

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Spar Panel Parametrization

Optimized parameters:• Configuration• Panel thickness• Stringer height• Stringer positions• Hole positions

Fixed parameters:• Length• Width• Loading

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Half VTP Test

• 45 panels• Non-realistic global loadcase• Ansys FE analyses• Limited set of configurations• No holes required• 20 Win-XP PCs @ 2.6GHz• 2 structure iterations• Runtime: ca. 8 hours.

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Neural Network Training

i1

i2

i3

h1

h2

h3

h4

h5

o1

o2

b1 b2

1 2

in = 2 3

3 4 3 5

5 7tar =

Error (tar - output)

Error Backpropagation

Network Simulation (Evaluation)

w11

w13

w12

w21

w22

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Genetic Algorithms

ParametrizationA = 3, 10, 100, 16

B = 11, 6, 140, 20

C = 5, 8, 120, 18

D = 11, 10, 40, 14

Population

FitnessFA = 55

FB = 40

FC = 43

FD = 47

calculation

3, 10, 100, 16 E = 3, 10, 120, 18

5, 8, 120, 18 F = 5, 8, 100, 16

3, 10, 100, 16

5, 8, 120, 18E = 4, 9, 110, 17

Crossover (A,C)

or

11, 6, 140, 20 E = 8, 6, 140, 20

11, 10, 40, 14 F = 11, 10, 100, 14

Mutation (B & D)

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Screenshot Wizard

41

Screenshot Master

Multilevel Distributed Structure Optimization Jorg Entzinger Roberto Spallino Wout Ruijter.

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Transcript of Multilevel Distributed Structure Optimization Jorg Entzinger Roberto Spallino Wout Ruijter.