Article2020

Multi-scale uncertainty quantification of composite laminated plate considering random and interval variables with data driven PCE method

Xiang Hua Peng · Tong Ye · Jiquan Li · Huaping Wu · Shaofei Jiang · Guohai Chen

34 citations0/0 files storedMechanics of Advanced Materials and Structures

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Probabilistic and Robust Engineering Design Composite Structure Analysis and Optimization Aeroelasticity and Vibration Control

Abstract

The paper proposes a multi-scale uncertainty quantification framework of composite laminated plates. The micro and macro uncertainty parameters are represented by random and interval variables according to available uncertainty information. The data-driven Polynomial Chaos Expansion model is constructed, where polynomial coefficients of random variables are determined based on their high-dimensional statistical information, and coefficients of interval variables are calculated using sampling analysis method. The proposed framework is applied for multi-scale buckling analysis, natural frequency calculation and reliability estimation. The comparison results with Monte Carlo Sampling method demonstrate the computational superiority of the proposed algorithm to achieve high accuracy in multiple conditions.

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References

2012Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion 2011Reliability in composites – A selective review and survey of current development 2010A critical reliability evaluation of fibre reinforced composite materials based on probabilistic micro and macro-mechanical analysis 2017Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment 2018Uncertainty quantification in multiscale simulation of woven fiber composites 2011Reliability based optimization of laminated composite structures using genetic algorithms and Artificial Neural Networks 2015Fuzzy uncertainty propagation in composites using Gram–Schmidt polynomial chaos expansion 2018Probabilistic micromechanical spatial variability quantification in laminated composites

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