A data driven polynomial chaos based approach for stochastic analysis of CFRP laminated composite plates

Sasikumar, P.; Venketeswaran, Abhishek; Suresh, R.; Gupta, Sayan

doi:10.1016/j.compstruct.2015.02.010

Cited by 32 publications

(10 citation statements)

References 41 publications

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“…128 All the principal directions were determined along with the typical plain weave E-glass/epoxy high strain rate compressive properties and plain weave carbon/epoxy. 134 These directions have shown that in comparison with quasi-static loading, the compressive strength is greater at high strain rate. The compressive strength improvements are observed with the increase in strain rate with thickness direction.…”

Section: Sensitivity Of Mechanical Response Of Composite Materials Anmentioning

confidence: 99%

Properties of multifunctional composite materials based on nanomaterials: a review

Ali

Andriyana

2020

RSC Adv.

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Section: Sensitivity Of Mechanical Response Of Composite Materials Anmentioning

confidence: 99%

Properties of multifunctional composite materials based on nanomaterials: a review

Ali

Andriyana

2020

RSC Adv.

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“…The probability density function of homogenized property and average strain for many SVEs considering randomness in both physical and geometrical parameters can be obtained by the Gaussian mixture distribution method as in Eqs. (2)(3). A Gaussian mixture distribution is used, because it can capture nonlinear formulation and quantify the randomness on the results.…”

Section: Microstructure Modeling Of Short Fiber Reinforced Compositesmentioning

confidence: 99%

“…Some popular SFEMs, such as the spectral method [1] including the Karhunen-Loeve (KL) expansion [2], the polynomial chaos (PC) expansion [3], and the stochastic perturbation method [4][5][6][7], are used to represent the input and output random fields. Computational cost in SFEM is smaller than that in the Monte Carlo simulation (MCS), because the accuracy of MCS depends on the large enough number of samples, especially when there are many random parameters.…”

Section: Introductionmentioning

confidence: 99%

Stochastic nonlinear multiscale computational scheme for short fiber reinforced composites to study the influence of microstructural variability on damage propagation

Hoang

Abe

Nakamura³

et al. 2020

SN Appl. Sci.

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In this paper, a stochastic nonlinear multiscale computational scheme is proposed to study the influence of the variability in both physical parameters of constituent materials and geometrical parameters such as fiber orientation, fiber arrangement of short fiber reinforced composites on the damage propagation. The aim is to predict many probable damage patterns in a huge number of scenarios under complex strain conditions and to discuss the influential level of variability in the physical and geometrical parameters for short fiber reinforced composites. The parameterization and numerical modelling for short fiber reinforced plastics made by injection molding are conducted. The random physical parameters are considered theoretically by the first-order perturbation based stochastic homogenization method, while sampling is used for the random geometrical parameters. When the scenario becomes huge in the stochastic nonlinear analysis, a computational scheme using sub-sampling is proposed. Moreover, the stochastic prediction of the homogenized properties of the composite material and their probable degradation are discussed. The stochastic computation of microscopic strains is analyzed though the damage propagation simulation. When the geometrical variability is considered, a definition of an average strain distribution among many samples is also presented. The efficiency of the accelerated element-by-element scaled conjugate gradient iterative solver is shown by solving many three-dimension random short fiber reinforced models with hundreds of thousands of degrees of freedom. The stochastic computational scheme provides a suitable reference to predict failure under specific reliability requirements of short fiber reinforced composites or other composite materials.

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“…Instead, surrogate model approaches, which are constructed based on a limited set of actual input/output data points, are a suitable method when dealing with such complex problems. Several methods of application of surrogate models have been reported in the literature for evaluation of uncertainties in composite laminates, such as Kriging method [15,16], radial basis function [17], polynomial chaos expansion (PCE) [18][19][20], and artificial neural network (ANN) [21,22]. State-of-the-art reviews on the surrogate models for evaluating the uncertainty in structural responses of composite laminates can be found in [23].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of mechanical behavior of flax fiber composites

Ravandi¹,

Banu²,

Noorian³

2020

Preprint

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The natural origin of the fibers combined with random production flaws results in significant uncertainties in the properties of natural fiber reinforced composites. A probabilistic assessment can help to characterize the uncertainties and evaluate the reliability of natural fiber composites, enabling their use in engineering designs. Toward this end, this study aims to quantify the uncertainties in the tensile strength and frequency response of a unidirectional flax/epoxy composite due to the variability of various input parameters, including the fiber material properties and manufacturing flaws. Based on the available data in the literature, the non-deterministic input variables were divided into normal and uniform variables using a statistical test. A computationally efficient response surface approach based on the polynomial chaos expansion was adopted to conduct the uncertainty analysis with multi-type uncertain variables. Moreover, the results were validated by the direct Monte Carlo simulation to demonstrate the accuracy and efficiency of the surrogate model.

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A data driven polynomial chaos based approach for stochastic analysis of CFRP laminated composite plates

Cited by 32 publications

References 41 publications

Properties of multifunctional composite materials based on nanomaterials: a review

Properties of multifunctional composite materials based on nanomaterials: a review

Stochastic nonlinear multiscale computational scheme for short fiber reinforced composites to study the influence of microstructural variability on damage propagation

Uncertainty analysis of mechanical behavior of flax fiber composites

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