An efficient multi-objective optimization framework for thin-walled tubular deployable composite boom

As a crucial structural component in space applications such as solar sails and solar arrays, the thin-walled tubular deployable composite booms (DCBs) demonstrate extensive utilization by employing stored elastic strain energy to achieve folding and deploying functions. This paper introduces a multi-objective optimization framework that integrates an analytical model with a genetic algorithm. By utilizing a multi-objective evolutionary algorithm based on de-composition (MOEA/D), the optimization objectives of minimizing folding moment and maximizing bending stiffness are pursued. Multiple constraints associated with failure avoidance, laminate stacking sequence design principles, and the folding moment range of actuator in the folding mechanism are considered in the optimization. The multi-objective optimization design of the tubular DCBs is performed to obtain the optimal combinations of cross-sectional radius, central angle, and ply scheme. Experimental validation confirms the efficacy of the optimization results. Additionally, an in-depth analysis on the influence of genetic algorithm types, hyperparameters, and different design variables on the optimization outcomes is thoroughly discussed. The findings of this study offer significantly insights for the practical engineering applications of tubular DCBs.