Application of Surrogate Based Particle Swarm Optimization to the Reliability-Based Robust Design of Composite Pressure Vessels
A surrogate based particle swarm optimization (SBPSO) algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability-based robust design (RBRD) of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method (performed by software ANSYS). An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.
Keywordsstructural optimization reliability based robust design composite pressure vessel surrogate based particle swarm optimization sequential algorithm
Unable to display preview. Download preview PDF.