Multi-objective Optimization for Coupled Mechanics-dynamics Analyses of Composite Structures

摘要

Fiber reinforced composites are increasingly used in advanced applications dueto advantageous qualities including high strength-to-weight ratio. The ability totailor composite structures to meet specific performance criteria is particularlydesirable. In practice designs must often balance multiple objectives withconflicting behavior. Objectives of this work were to optimize lamina orientationsof a three-ply carbon fiber reinforced composite structure for the coupled solidmechanics and dynamics considerations of minimizing max principal stress whilemaximizing fundamental frequency. Two approaches were investigated: Pareto setoptimization (PSO), and multi-objective genetic algorithm (MOGA). In PSO, asingle objective function is constructed as a weighted sum of multiple objectiveterms. Multiple weighting sets are evaluated to determine a Pareto set of solutions.MOGA mimics evolutionary principles, where the best design points populatesubsequent generations. Instead of weight factors, MOGA uses a domination countthat ranks population members. Results showed both methods converged tosolutions along the same Pareto front. The PSO method calculated fewer functionevaluations, but provided many fewer final data points. At a certain threshold,MOGA provides more solutions with fewer calculations. The PSO method requiresmore user intervention which may introduce bias, but can largely be run in parallel.In contrast, MOGA generation are evaluated in series. The Pareto front showed thetrend of increasing frequency with increasing stress. At the low stress andfrequency extreme, the stacking sequence tended toward (45°/90°/45°) with maxprincipal stress located in the inner ply in the hoop direction. At high stress andfrequency, the stacking sequences (90°/*/90°) indicated that the middle plyorientation was less significant. A mesh convergence study and dynamic validationexperiments gave confidence to the computational model. Future work will includean uncertainty quantification about selected solutions. The final selected solutionwill be fabricated and experimental validation testing will be conducted.