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Acta Aeronautica et Astronautica Sinica ›› 2026, Vol. 47 ›› Issue (3): 232214.doi: 10.7527/S1000-6893.2025.32214

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles    

RBF-enhanced direct probability integral method for stochastic buckling analysis of plate and shell structures

Chaofan WANG1, Huanlin ZHOU1, Xuan WANG1,2()   

  1. 1.Department of Engineering Mechanics,Hefei University of Technology,Hefei 230009,China
    2.State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology,Dalian 116024,China
  • Received:2025-05-08 Revised:2025-08-11 Accepted:2025-09-08 Online:2025-09-19 Published:2025-09-18
  • Contact: Xuan WANG E-mail:xuanwang@hfut.edu.cn
  • Supported by:
    National Natural Science Foundation of China(12202129);Open Project of State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment(GZ23105)

Abstract:

As critical load-bearing components in aerospace, marine, and construction engineering fields, the buckling stability of plate and shell structures directly determines the safety and reliability of entire systems. However, factors such as material property variability significantly influence the distribution characteristics of critical buckling loads, the traditional deterministic analysis methods are hard to quantify the stochastic influence. The Radial Basis Function (RBF)-enhanced Direct Probability Integral Method (DPIM) is proposed to efficiently determine the probabilistic characteristics of buckling critical loads in plate and shell structures under multiple random variables, which provides theoretical foundations for stochastic buckling uncertainty quantification. By constructing a high-precision explicit surrogate model between critical buckling loads and random variables using RBF, this method effectively reduces the computational cost for implementing the time-consuming buckling finite element analysis in the original direct probability integral method. Comparative analyses with traditional DPIM and Monte Carlo simulation methods in numerical examples demonstrate that the proposed RBF-enhanced DPIM achieves remarkable computational efficiency improvements while maintaining controlled accuracy loss.

Key words: plate and shell structure, buckling analysis, direct probability integral method, radial basis function, stochastic mechanics

CLC Number: