A Gaussian process-driven worst realistic imperfection method for cylindrical shells by limited data. (December 2022)

• Record Type: Journal Article
• Authors: Feng, Shaojun; Duan, Yuhui; Yao, Chongyang; Yang, Hao; Liu, Hao; Wang, Bo; Hao, Peng
• Is Part Of: Thin-walled structures. Volume 181(2022)
• Publication Date: 2022-12
• Subjects: Geometric imperfections -- Cylindrical shell -- Buckling load -- Gaussian process -- Stochastic Kriging -- Limited data expansion; Thin-walled structures -- Periodicals; 690.1
• DOI: 10.1016/j.tws.2022.110130
• Languages: English
• ISSNs: 0263-8231

Abstract

Abstract: In this study, we propose a Gaussian process-driven worst imperfection method to generate simulated geometric imperfections and find the lower limit of buckling capability. First, we utilize the nested stochastic Kriging to estimate the global trend and envelopes of the geometric imperfections. Second, the expansion optimal linear estimation and direct weighting construction method are used to expand limited data, which allow the simulated geometric imperfections to have the same characteristics as the initial data. Third, two magnitude-based constraints are provided, which can make the simulated geometric imperfections more consistent with the realistic data and can represent more design space. Finally, for the cylindrical shells, the data pre-processing method is further provided in this paper. Two numerical examples with assumed geometric imperfections are used to validate the proposed method, and the minimum buckling capacity of a cylindrical shell with 8 realistic geometric imperfections data is estimated. The results show that the proposed method can achieve the best performance in terms of accuracy, efficiency, and robustness. Highlights: A Gaussian process-driven worst imperfection (GPWI) method is proposed to find the lowest buckling capability for shells. The nested stochastic Kriging (NSK) to estimate the global trend and envelopes of the geometric imperfections. We propose the Magnitude-based probability envelopes strategy (MPES) as a probabilistic constraint.