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Multilevel Distributed

Develop a tool to minimize aircraft substructure weight with autonomous analysis run for new design features. Multilevel distributed structure optimization for vertical tail plane spar panel configurations. Includes finite element models and optimization algorithm details.

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Multilevel Distributed

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  1. MultilevelDistributed Structure Optimization Jorg Entzinger Roberto Spallino Wout Ruijter

  2. Outline • Introduction • Problem description • Program design • Tests and test results • Conclusion

  3. 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. Problem Formulation

  4. Vertical Tail Plane .

  5. Vertical Tail Plane .

  6. Spar Panel Configurations

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

  8. Optimization Problem

  9. Multilevel Implementation

  10. Structure Level Optimization Initialize structure Calculate component loadings and BCs Converged? Postprocess Optimize component 1 Optimize component N ......

  11. 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

  12. Component Level Optimization Population FE solver Selection Crossover Mutation Converged? Optimum

  13. Component Level Optimization Population Training data set Neural Networks FE solver Selection Crossover Mutation Converged? Optimum

  14. Component Level Optimization Population Training data set Neural Networks FE solver Selection Crossover Mutation Accuracy check (FE) Converged? Optimum

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

  16. 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

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

  18. Convergence

  19. Neural Network Accuracy

  20. 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

  21. 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

  22. 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

  23. Front Spar Panels

  24. Front Spar Panels • Many stiffeners in lower spar panels (to prevent buckling)

  25. Front Spar Panels • Many stiffeners in lower spar panels (to prevent buckling) • Holes found where not demanded

  26. 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

  27. Rear Spar Panels

  28. Rear Spar Panels • More longitudinal stiffeners might me beneficial (compare with front spar!) • Conclusion: add configurations

  29. Full VTP Test • 90 components • Non-realistic global loadcase • Limited set of configurations • No holes required in upper 4 panels

  30. Full VTP Test • 90 components • Non-realistic global loadcase • Limited set of configurations • No holes required in upper 4 panels • 27 Win-XP PCs @ 2.6GHz • 3 structure iterations • Runtime: ca. 9 hours.

  31. 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

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

  33. Questions? Jorg Entzinger Roberto Spallino Wout Ruijter

  34. Spar Panel Parametrization

  35. Optimized parameters: Configuration Panel thickness Stringer height Stringer positions Hole positions Fixed parameters: Length Width Loading Spar Panel Parametrization

  36. 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.

  37. Neural Network Training Network Simulation (Evaluation) h1 1 2 in = 2 3 3 4 h2 i1 o1 Error (tar - output) h3 i2 o2 i3 h4 3 5 5 7 tar = h5 b1 b2 Error Backpropagation  w11  w21   w12  w22  w13

  38. 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

  39. Screenshot Wizard

  40. Screenshot Master

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