Hybrid-surrogate-based prediction and reliability-based optimization of curling strength for bistable cylindrical shells

Bistable composite thin-walled column–shells combine high specific stiffness and compact stowage, making them critical elements in aerospace deployable structures. Their curling and deployment involve strong geometric nonlinearities and stress concentrations near edges and transition zones, while material and manufacturing uncertainties complicate reliable design. This paper presents a hybrid surrogate model that couples enhanced Kriging, polynomial chaos expansion, and a radial basis function neural network to accurately predict curling strength under uncertainty. Leveraging the HSM (hybrid surrogate model), a derivative-based global sensitivity measure is employed to identify the dominant design variables, and a reliability-based design optimization is utilized to minimize the probability of matrix tensile failure. Numerical validation demonstrates that the proposed framework achieves a favorable balance between predictive accuracy and computational efficiency, substantially improving the reliability and engineering applicability of bistable composite structures.