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Stiffened Composite Panel Design

Stiffened Composite Panel Design . Based on “Improved genetic algorithm for the design of stiffened composite panels,” by Nagendra , Jestin , Gurdal, Haftka , and Watson, Computers and Structures, pp. 543-555, 1996.

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Stiffened Composite Panel Design

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  1. Stiffened Composite Panel Design • Based on “Improved genetic algorithm for the design of stiffened composite panels,” by Nagendra, Jestin, Gurdal, Haftka, and Watson, Computers and Structures, pp. 543-555, 1996. • Standard genetic algorithm did not work well enough even with simplified structural model (finite strip). • Algorithm was improved based on simplified version of the panel design problem (e.g. fixed blade height, single laminate).

  2. Geometry and loading

  3. Modeling in PASCO • Finite strip model assume that in one direction we can use sine solution, while in the other the displacement can have general shape. • Panel Analysis and sizing code (Stroud and Anderson) based on analysis code by Wittrick and Williams.

  4. Optimization problem • Minimize the weight of the panel • Design variables ply angles of skin (), ply angles of blade ()and flange (same), blade height. • Outer plies limited tofor damage tolerance. • Constraints: Buckling load multiplier, strain-failure load multiplier, balanced laminates, no more than four contiguous plies of same orientation.

  5. Optimization formulation • Constrained version • Plies in stacks of two. • Unconstrained version • Contiguity violation: Number of contiguous zero or ninety stacks in excess of 2 (for example 2 for

  6. Material properties • Today’s graphite-epoxys can do much better.

  7. Genetic code

  8. Selection and Crossover • Rank based fitness and roulette wheel selection. • Original crossover is a 2-point crossover applied to entire genome. • Two children produced. • Improved crossover applied individually to each of the three substrings. • Crossover applied with 95% probability. If not, first parent copied into next generation.

  9. Mutations • Mutation applied to one child with each gene mutated with 3% probability to random new gene. • Improved mutation separates orientation mutations from deletion and addition mutations. • Stack deletion: First select randomly skin or blade. Then stack closest to mid-plane deleted with Probability of 2-3%. • Stack addition: Skin or blade selected randomly, then random stack added at mid-plane. • New: Permutation, intra-laminar swap, inter-laminar swap.

  10. Results with original GA • What is the main difference between rounded continuous optimum and GA design?

  11. Tuning the algorithm • Probabilities associated with the different operators tuned on a simplified problem. • For simplified problem, the blade laminate and blade height was fixed based on previous results. • This reduced number of designs from to

  12. Improved GA designs • What is different?

  13. Comparison

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