A D-vine copula-based coupling uncertainty analysis for stiffness predication of variable-stiffness composite

TL;DR

This paper introduces a novel coupling uncertainty analysis method using D-vine copulas and Bayesian model selection to efficiently predict the stiffness of variable-stiffness composites, incorporating neural networks for enhanced computational efficiency.

Contribution

It proposes a new Bayesian copula model selection method combined with a D-vine copula approach and neural network acceleration for uncertainty analysis in composite stiffness prediction.

Findings

BPNN achieves higher efficiency and sufficient accuracy compared to reanalysis.

The method effectively captures the coupling of random variables in composite materials.

Numerical examples verify the feasibility and accuracy of the proposed approach.

Abstract

This study suggests a coupling uncertainty analysis method to investigate the stiffness characteristics of variable stiffness (VS) composite. The D-vine copula function is used to address the coupling of random variables. To identify the copula relation between random variables, a novel one-step Bayesian copula model selection (OBCS) method is proposed to obtain a suitable copula function as well as the marginal CDF of random variables. The entire process is Monte Carlo simulation (MCS). However, due to the expensive computational cost of complete finite element analysis (FEA) in MCS, a fast solver, reanalysis method is introduced. To further improve the efficiency of entire procedure, a back propagation neural network (BPNN) model is also introduced based on the reanalysis method. Compared with the reanalysis method, BPNN shows a higher efficiency as well as sufficient accuracy. Finally, the fiber angle deviation of VS composite is investigated by the suggested strategy. Two numerical examples are presented to verify the feasibility of this method.