A micromechanics‐based inverse study for stochastic order reduction of elastic UD fiber reinforced composites analyses

Wu, Ling; Adam, Laurent; Noels, Ludovic

doi:10.1002/nme.5903

Cited by 6 publications

(20 citation statements)

References 52 publications

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“…First the case of linear elasticity previously developed in [50] is briefly recalled. Then the non-linear elasto-plastic case and the damage-enhanced non-linear case are successively derived.…”

Section: Stochastic Mf-rom For Non-linear Compositesmentioning

confidence: 99%

“…The inverse MFH procedure of linear elastic composites is recalled briefly for the sake of completeness, more details are given in [50]. In order to reproduce with MFH the randomness observed by computational homogenization on different SVE realizations, the MFH micro-structure geometrical and phases material properties are represented by random variables whose observations are identified from an inverse analysis.…”

Section: Random Descriptors Of Linear Elastic Compositesmentioning

confidence: 99%

“…• The effective anisotropic elasticity tensor of fibers C el I , which can be considered to be constant for all the SVE realizations [50].…”

Section: Random Descriptors Of Linear Elastic Compositesmentioning

confidence: 99%

“…In [50], such a methodology has been developed in the context of linear elasticity by using as stochastic ROM basis the non-linear Mean-Field Homogenization (MFH) theory, thus defining a stochastic Mean-Field Reduced Order Model (MF-ROM). The MFH approach is originally a computationally efficient semi-analytical framework for the modeling of multi-phase composites.…”

Section: Introductionmentioning

confidence: 99%

“…The stochastic MF-ROM is defined in Section 3 by conducting an inverse identification of the model parameters from the full-field analyzes. First the case of linear elasticity treated in [50] is briefly summarized, then the cases of elastic fibers embedded in elasto-plastic and damage-enhanced elasto-plastic matrix are successively developed. Note that in the case of damage-enhanced elasto-plasticity, the formalism pre-sented is strictly valid up to the strain-softening onset of the meso-scale homogenized behavior.…”

Section: Introductionmentioning

confidence: 99%

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An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites

Nguyễn

Adam³

et al. 2019

Computer Methods in Applied Mechanics and Engineering

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An inverse Mean-Field Homogenization (MFH) process is developed to improve the computational efficiency of non-linear stochastic multiscale analyzes by relying on a micro-mechanics model. First full-field simulations of composite Stochastic Volume Element (SVE) realizations are performed to characterize the homogenized stochastic behavior. The uncertainties observed in the non-linear homogenized response, which result from the uncertainties of their micro-structures, are then translated to an incrementalsecant MFH formulation by defining the MFH input parameters as random effective properties. These effective input parameters, which correspond to the micro-structure geometrical information and to the material phases model parameters, are identified by conducting an inverse analysis from the full-field homogenized responses. Compared to the direct finite element analyzes on SVEs, the resulting stochastic MFH process reduces not only the computational cost, but also the order of uncertain parameters in the composite micro-structures, leading to a stochastic Mean-Field Reduced Order Model (MF-ROM). A data-driven stochastic model is then built in order to generate the random effective properties under the form of a random field used

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Section: Stochastic Mf-rom For Non-linear Compositesmentioning

confidence: 99%

Section: Random Descriptors Of Linear Elastic Compositesmentioning

confidence: 99%

“…• The effective anisotropic elasticity tensor of fibers C el I , which can be considered to be constant for all the SVE realizations [50].…”

Section: Random Descriptors Of Linear Elastic Compositesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites

Nguyễn

Adam³

et al. 2019

Computer Methods in Applied Mechanics and Engineering

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A micromechanical mean‐field homogenization surrogate for the stochastic multiscale analysis of composite materials failure

Calleja Vázquez,

Wu,

Nguyen

et al. 2023

Numerical Meth Engineering

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SummaryThis paper presents the construction of a mean‐field homogenization (MFH) surrogate for nonlinear stochastic multiscale analyses of two‐phase composites that allows the material response to be studied up to its failure. The homogenized stochastic behavior of the studied unidirectional composite material is first characterized through full‐field simulations on stochastic volume elements (SVEs) of the material microstructure, permitting to capture the effect of the microstructural geometric uncertainties on the material response. Then, in order to conduct the stochastic nonlinear multiscale simulations, the microscale problem is substituted by a pressure‐dependent MFH reduced order micromechanical model, that is, a MF‐ROM, whose properties are identified by an inverse process from the full‐field SVE realizations. Homogenized stress‐strain curves can be used for the identification process of the nonlinear range, however, a loss of size objectivity is encountered once the strain softening onset is reached. This work addresses this problematic introducing a calibration of the energy release rate obtained with a nonlocal MFH micromechanical model, allowing to scale the variability found on each SVE failure characteristics to the macroscale. The obtained random effective properties are then used as input of a data‐driven stochastic model to generate the complete random fields used to feed the stochastic MF‐ROM. To show the consistency of the methodology, two MF‐ROM constructed from SVEs of two different sizes are successively considered. The performance of the MF‐ROM is then verified against an experimental transverse‐compression test and against full‐field simulations through nonlocal Stochastic Finite Element Method (SFEM) simulations. The implementation of the energy release rate calibration allows to conduct stochastic studies on the failure characteristics of material samples without the need for costly experimental campaigns, paving the way for more complete and affordable virtual testing.

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AI/ML for Quantification and Calibration of Property Uncertainty in Composites

Pitz

Pochiraju

2022

Machine Learning Applied to Composite Materials

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A micromechanics‐based inverse study for stochastic order reduction of elastic UD fiber reinforced composites analyses

Cited by 6 publications

References 52 publications

An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites

An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites

A micromechanical mean‐field homogenization surrogate for the stochastic multiscale analysis of composite materials failure

AI/ML for Quantification and Calibration of Property Uncertainty in Composites

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