A data-driven statistical inverse identification method for phase field modeling of fracture in random heterogeneous elastic media - Université Gustave Eiffel Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

A data-driven statistical inverse identification method for phase field modeling of fracture in random heterogeneous elastic media

Résumé

This research work concerns the forward numerical simulation and statistical inverse identification of a phase field model for fracture in random heterogeneous elastic materials. Within the framework of linear elasticity theory and probability theory, a stochastic model for almost surely (a.s.) isotropic random elastic materials adapted to standard phase field models for brittle fracture is proposed and constructed using the maximum Entropy (MaxEnt) principle. A sensitivity analysis is carried out to study the influence of some fracture properties (fracture toughness) and random spatially-varying elastic material properties on the crack path and the global force-displacement response. Finally, a data-driven statistical inverse identification method based on a nonparametric Bayesian approach is proposed to estimate the posterior probability distribution of the random fracture toughness. The proposed approach is illustrated on two classical benchmark problems for brittle fracture, namely a mode I fracture problem (uniaxial tension test) and a mode II fracture problem (pure shear test) of a two-dimensional single-edge notched/cracked square specimen.
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Dates et versions

hal-04122931 , version 1 (08-06-2023)

Licence

Domaine public

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  • HAL Id : hal-04122931 , version 1

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Florent Pled, Christophe Desceliers. A data-driven statistical inverse identification method for phase field modeling of fracture in random heterogeneous elastic media. Engineering Mechanics Institute Conference 2023 (EMI 2023), Georgia Institute of Technology, Jun 2023, Atlanta, United States. ⟨hal-04122931⟩
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