Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment

Abstract

This paper presents a concise state-of-the-art review along with an exhaustive comparative investigation on surrogate models for critical comparative assessment of uncertainty in natural frequencies of composite plates on the basis of computational efficiency and accuracy. Both individual and combined variations of input parameters have been considered to account for the effect of low and high dimensional input parameter spaces in the surrogate based uncertainty quantification algorithms including the rate of convergence. Probabilistic characterization of the first three stochastic natural frequencies is carried out by using a finite element model that includes the effects of transverse shear deformation based on Mindlin's theory in conjunction with a layer-wise random variable approach. The results obtained by different metamodels have been compared with the results of traditional Monte Carlo simulation (MCS) method for high fidelity uncertainty quantification. The crucial issue regarding influence of sampling techniques on the performance of metamodel based uncertainty quantification has been addressed as an integral part of this article.

Toward Variability Characterization and Statistic Models’ Constitution for the Prediction of Exponentially Graded Plates’ Static Response

“…From the literature review, it was possible to conclude that the subject of uncertainty in the structural performance constitutes a scientifically relevant subject where several important topics require a continued research investment effort. This is also a global conclusion that can be drawn from the review papers due to Sriramula and Chryssanthopoulos [2] and Dey et al [3], in a broader context of the composite materials. According to Sriramula and Chryssanthopoulos [2], a multidisciplinary perspective ranging from a random variable-based stochastic computational mechanics approach, morphology-based random composite modeling, stiffness/strength or fracture mechanics-based models, or component level-based uncertainty modeling, may be a promising direction for a stochastic analysis of composite structures.…”

“…According to Sriramula and Chryssanthopoulos [2], a multidisciplinary perspective ranging from a random variable-based stochastic computational mechanics approach, morphology-based random composite modeling, stiffness/strength or fracture mechanics-based models, or component level-based uncertainty modeling, may be a promising direction for a stochastic analysis of composite structures. Dey et al [3] addressed a complementary investigation on surrogate models, which was verified against Monte Carlo simulation results. The influence of sampling techniques on the uncertainty quantifications of different metamodels was also studied.…”

Effective in-plane elastic moduli of quasi-random spatially irregular hexagonal lattices

“…For dynamic and quasi-static analysis, separate nite element modelling of the honeycomb core in a sandwich structure may increase the degrees of freedom for the entire system up to such an extent that makes the overall process unmanageably complex and prohibitively expensive for simulation. In case of uncertainty quantication using a Monte Carlo based approach, the problem aggravates as a large number of expensive nite element simulations are needed to be carried out (Dey et al, 2017a(Dey et al, ,b, 2016aHurtado and Barbat, 1998;Mahata et al, 2016;Mukhopadhyay, 2017;Mukhopadhyay et al, 2015. Application of surrogate based approaches to achieve computational eciency, as adopted in many of these papers, does not make the analysis physically insightful and this approach often suer from lack of condence in the predicted results.…”

Frequency domain homogenization for the viscoelastic properties of spatially correlated quasi-periodic lattices

“…comb core in a sandwich structure may increase the degrees of freedom for the entire system up to such an extent that can make the overall process unmanageably complex and prohibitively expensive for simulation. In case of uncertainty quantification using a Monte Carlo based approach, the problem aggravates as large number of expensive finite element simulations are needed to be carried out (Dey et al, 2017(Dey et al, , 2016aHurtado and Barbat, 1998;Mahata et al, 2016;Mukhopadhyay, 2017;Mukhopadhyay et al, 2015. Application of surrogate based approaches to achieve computational efficiency, as adopted in many of these papers, does not make the analysis physically insightful and this approach often suffer from lack of confidence in the predicted results.…”