A multi-fidelity competitive sampling method for surrogate-based stacking sequence optimization of composite shells with multiple cutouts. (1st June 2020) - Tian, Kuo; Ma, Xiangtao; Li, Zengcong; Lin, Shiyao; Wang, Bo; Waas, Anthony M.
Abstract: In order to explore the global optimizing ability of the surrogate-based stacking sequence optimization of composite shells with multiple cutouts, a multi-fidelity competitive sampling (MFCS) method is developed in this paper, which is composed of a low-fidelity sampling level and a high-fidelity sampling level. In the low-fidelity sampling level, competitive sampling points are determined by means of the prior screening criterion and the further screening criterion in sequence. Based on the POD-based buckling method, a novel reduced-order model is established as the low-fidelity model for fast linear buckling analysis of composite shells with multiple cutouts. In the high-fidelity sampling level, the explicit dynamic method is employed to calculate the post-buckling response of competitive sampling points. Based on these sampling results, a surrogate model is built. Next, a surrogate-based stacking sequence optimization framework is established for minimum material cost of composite shells with multiple cutouts. A simple optimization example of Mishra's Bird test function is performed and the effectiveness of the MFCS method is demonstrated. Finally, the proposed method is used for the surrogate-based stacking sequence optimization of composite shells with multiple cutouts. The optimal result of total material cost by the MFCS method decreases by 23.0% than the optimal result by the traditional sampling method, which indicates that the MFCS method contributes to improving the global optimizing ability of the surrogate-based stacking sequence optimization of composite shells with multiple cutouts.