Multi-particle swarm optimization used to study material degradation in aeroelastic composites including probabalistic uncertainties

Extensive research has been done in optimizing simple composite wings to maximize their flutter/divergence speeds by varying ply orientation angles. This paper extends upon previous work by simulating material degradation in multiple layers, thereby adding a dynamic component to the optimization problem. A multiple-swarm variation (MPSO*) of the canonical particle swarm algorithm is used in this paper, along with a Non-Intrusive Polynomial Chaos (NIPC) model to estimate a mean flutter speed and obtain a robust optimal ply orientation. Results show that employing the MPSO* algorithm is well suited to track the time-varying optimum ply orientation. As expected, good correlation is also obtained between the NIPC analysis and Monte-Carlo simulation, and this was observed even with second and third order polynomials with modest oversampling. Results also indicate Young's modulus variations in the outer layer impact flutter velocity much more than the middle layer, however only a small change is noted in the optimal robust topology compared to the deterministic orientation.