stiffened composite panel design
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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).
Geometry and loading
20,000 /5,000 /
x
xy
N lb inN lb in
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.
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.
2 2
4 2
960 , 30 , 72
s s b b b f
s b b f
W t n A n A n A
A in A H A in
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
,( , , )
11
0
i bb b sH
b
s
Minimize W H n n
Suchthat
g
2
2
1
1
min ,
cont failfail
fail cont fail
fail b s
W PF
W P
110
sk blcont
nv nvP
Material properties
• Today’s graphite-epoxys can do much better.
Genetic code
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.
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.
Results with original GA
• What is the main difference between rounded continuous optimum and GA design?
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
Improved GA designs
• What is different?
Comparison