Multilevel Distributed

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Multilevel Distributed

• Structure Optimization

Jorg Entzinger Roberto Spallino Wout Ruijter
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Outline

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

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Problem Formulation

• Develop a design tool to
• minimize the weight
• of an aircraft substructure
• subjected to static loadcases.
• New design features must be analyzed
• in an autonomous, overnight run.

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Vertical Tail Plane .
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Vertical Tail Plane .
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Spar Panel Configurations
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Finite Element Models

• Linear static analyses
• buckling multiplier
• maximum strain
• FEM models are parametric
• About 8000 nodes quadratic 3D shell (48000 DOF)

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Optimization Problem
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Multilevel Implementation
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Structure Level Optimization
Initialize structure
Calculate component loadings and BCs
Converged?
Postprocess
Optimize component 1
Optimize component N
......
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Component Level Optimization
Population
Set of possible solutions Calculation of pseudo
objective (objective penalties) Ranking based
on pseudo objective Interchange of parameter
values Random change of param. values
FE solver
Selection
Crossover
Mutation
Converged?
Optimum
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Component Level Optimization
Population
FE solver
Selection
Crossover
Mutation
Converged?
Optimum
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Component Level Optimization
Population
Training data set
Neural Networks
FE solver
Selection
Crossover
Mutation
Converged?
Optimum
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Component Level Optimization
Population
Training data set
FE solver
Neural Networks
Selection
Crossover
Mutation
Accuracy check (FE)
Converged?
Optimum
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Algorithm Overview

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

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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

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Tests

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

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Convergence
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Neural Network Accuracy
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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

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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

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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 _at_400 MHz
• Runtime ca. 18 hours

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Front Spar Panels
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Front Spar Panels

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

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Front Spar Panels

• Many stiffeners in lower spar panels (to prevent
  buckling)
• Holes found where not demanded

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Front Spar Panels

• Many stiffeners in lower spar panels (to prevent
  buckling)
• Holes found where not demanded
• More stiffeners in upper spar panels might be
  beneficial

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Rear Spar Panels
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Rear Spar Panels

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

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Full VTP Test

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

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Full VTP Test

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

• 27 Win-XP PCs _at_ 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

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Prospects

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

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Questions?
Jorg Entzinger Roberto 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 _at_ 2.6GHz
• 2 structure iterations
• Runtime ca. 8 hours.

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Neural Network Training
Network Simulation (Evaluation)
h1
1 2 in 2 3 3 4
i1
h2
o1
Error (tar - output)
i2
h3
o2
i3
h4
3 5 5 7
h5
tar
b1
b2
Error Backpropagation
? w11
? w21
?
? w12
? w22
? w13
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Genetic Algorithms
Population
FA 55 FB 40 FC 43 FD 47
A 3, 10, 100, 16 B 11, 6, 140, 20 C
5, 8, 120, 18 D 11, 10, 40, 14
Parametrization
Fitness
calculation
Crossover (A,C)
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, 18
or
E 4, 9, 110, 17
Mutation (B D)
11, 6, 140, 20 E 8, 6, 140, 20 11,
10, 40, 14 F 11, 10, 100, 14
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Screenshot Wizard
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Screenshot Master
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