Multiscale modeling of failure in composites under model parameter uncertainty

Bogdanor, Michael; Oskay, Caglar; Clay, Stephen B.

doi:10.1007/s00466-015-1177-7

Cited by 49 publications

(17 citation statements)

References 41 publications

(50 reference statements)

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“…A Gaussian mixture distribution is used, because it can capture nonlinear formulation and quantify the randomness on the results. It is also a probabilistic model that assumes all the results of many models are represented by a mixture of Gaussian distributions with random parameters [34]. In this work, j max is the maximum number of fiber orientation samples and i max is the maximum number of fiber arrangement samples concerning j.…”

Section: Microstructure Modeling Of Short Fiber Reinforced Compositesmentioning

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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Section: Microstructure Modeling Of Short Fiber Reinforced Compositesmentioning

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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“…ML algorithms are receiving growing attention in different applications for engineering problems (Adeli, ; Bogdanor, Mahadevan, & Oskay, ; Bogdanor, Oskay, & Clay, ; Lin, Nie, & Ma, ; Nabian & Meidani, ; Rafiei & Adeli, ; Reich, ; Zhang & Oskay, ). The growing capabilities and possibilities to obtain big databases to use as training sets for ML models make it a convenient and effective tool for the solution of problems that are in general too expensive, or when the time required for the solution is not acceptable with respect to the need they are trying to address.…”

Section: Data‐based “System” Scale Modeling Of Bepmentioning

confidence: 99%

Multiscale modeling of backward erosion piping in flood protection system infrastructure

Fascetti

Oskay

2019

Computer aided Civil Eng

Self Cite

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This article presents a novel multiscale modeling approach to simulate the evolution of the backward erosion piping (BEP) process in flood protection systems (FPSs). A multiphase description of the BEP phenomenon is proposed for the numerical solution at the local scale and validated by means of full‐scale experimental results available in the literature. Results of the local scale simulations are used as the training set for a multilayer machine learning (ML) model to bridge the information between the local and system scales. Accuracy of the trained ML algorithms is demonstrated by comparing results obtained from detailed physics‐based numerical models. The novelty of the proposed methodology lies in its capability of real‐time predictions of the overall response at the system scale. A case study is presented where a portion of the Nashville Metro Levee System is analyzed over the span of a year, to assess the likelihood of BEP in the infrastructure. The capability of the model to accept water height data obtained from field measurements is exploited in the numerical simulations.

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“…These composites are manufactured using several techniques such as pultrusion, filament winding, and prepreg production processes [12,13]. All of which introduce a level of uncertainty within constituent phases in terms of material property and geometrical uncertainties [14,15]. In this study, the continuous fibre-reinforced composites example used is a Boron-Aluminium composite with the properties shown in Table 1.…”

Section: Micro-scale Uncertaintiesmentioning

confidence: 99%

Influence of micro-scale uncertainties on the reliability of fibre-matrix composites

Omairey

Dunning

Sriramula

2018

Composite Structures

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This study investigates the effect of micro-scale geometric and material property uncertainties on the elastic properties and reliability of fibre reinforced composite materials. Composite materials are often designed using conservative design factors to account for a limited understanding of how multiscale uncertainties effect reliability. Structural reliability analysis can produce more efficient designs, but requires an understanding of how all sources uncertainty effect probability of failure. Previous studies have not considered micro-scale geometrical uncertainties and their combinations in a multiscale probabilistic-based reliability framework. Thus, this study will investigate the effect of numerous combinations of micro-scale material property and geometric uncertainties on the homogenised elastic properties. Furthermore, to account for the effect in a reliability-based framework, a novel surrogate modelling technique is developed to represent the uncertainties efficiently. The study concluded that the geometrical fibre stacking uncertainty is as influential as the widely investigated constituent material stiffness uncertainties. Consequently, representing the micro-scale geometric uncertainties within the developed multi-scale probabilistic-based framework improves the estimated stiffness. Thus probability of failure is reduced, compared with considering material property uncertainties only. Moreover, the framework clarified and highlighted the importance of representing fibre geometrical stacking uncertainty for a deeper understanding of their effect on composite stiffness properties.

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Multiscale modeling of failure in composites under model parameter uncertainty

Cited by 49 publications

References 41 publications

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

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

Multiscale modeling of backward erosion piping in flood protection system infrastructure

Influence of micro-scale uncertainties on the reliability of fibre-matrix composites

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