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Stiffened Composite Panel Design Based on Improved genetic algorithm for the design of stiffened composite panels, by Nagendra, Jestin, Gurdal, Haftka,

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Presentation on theme: "Stiffened Composite Panel Design Based on Improved genetic algorithm for the design of stiffened composite panels, by Nagendra, Jestin, Gurdal, Haftka,"— Presentation transcript:

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 , 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

5 Optimization formulation

6 Material properties Todays 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

12 Improved GA designs What is different?

13 Comparison


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