Large-scale sandwich structures optimization using Bayesian method

Abstract

Benefiting from advanced features like high stiffness-to-weight ratios, sandwich structures are widely used in aerospace for primary and secondary structures. As tasks grow more complex and structures increase in scale, high-dimensional design spaces inevitably arise. Optimizing large-scale sandwich structures efficiently and intelligently presents certain challenges. Additionally, functional requirements and constraints, such as thermal deformation, should be fully considered in the design of practical structures like solar arrays, which involve a large expensive analysis and make the problem more complicated. This paper proposes to use efficient Bayesian optimization with the active subspace (AC) method to address this type of problem. The active subspace method, combined with the adaptive kriging and global sensitivity analysis (GSA), is employed for dimension reduction and reconstructing the design space. Then the structure is optimized within the reconstructed design space using the efficient Bayesian method. The proposed optimization strategy is applied to a case study of solar array with sandwich panels, demonstrating that the developed framework is feasible and effective for structural optimization of large-scale sandwich structures.