A data-driven computational optimization framework for designing thin-walled lenticular deployable composite boom with optimal load-bearing and folding capabilities.

• The mechanical behaviors of LDCB were numerically simulated and verified. • A data-driven computational optimization framework was proposed. • DCQGA-GPR model with higher stability and prediction accuracy was proposed. • DCQGA-GPR-NSGA-III coupling technology proposed was determined as an excellent optimization strategy by benchmarking five algorithms. • The optimized LDCB design exhibits a significant performance improvement. The thin-walled lenticular deployable composite boom (LDCB) is promising for aerospace engineering applications due to its lightweight and compact nature, but its mechanical behaviors are the main challenges limiting its practical application. Here, the axial compression and folding behaviors of LDCB were numerically simulated and the simulation results were verified according to the experimental data in the literature. Then, a new double chains quantum genetic algorithm (DCQGA)-gaussian process regression (GPR) model and data-driven computational optimization framework were proposed and the new model was trained using a database with 400 simulation results, the superiority of which was demonstrated by prediction accuracy evaluation. Additionally, benchmarking five state-of-the-art algorithms found that the coupling technology of DCQGA-GPR-non-dominated sorting genetic algorithm III (NSGA-III) is an excellent optimization strategy to obtain a well-designed structure of LDCB that maximizes the effectiveness of the material while satisfying the lightweight. The optimized LDCB design exhibits a significant performance improvement, with a 26.3 % reduction in the folding moment, a 34.2 % reduction in the maximum Tsai-Hill failure index, a 43.6 % increase in the critical load, and an 11 % reduction in linear density of mass compared to the initial design. [Display omitted] [ABSTRACT FROM AUTHOR]