DANOUN, Aymen; PRULIÈRE, Etienne; CHEMISKY, Yves

doi:10.1016/j.mechmat.2022.104436

Métadonnées

Afficher la notice complète

Licence d’utilisation du document

DANOUN, Aymen

Institut de Mécanique et d'Ingénierie [I2M]

PRULIÈRE, Etienne
Institut de Mécanique et d'Ingénierie [I2M]

CHEMISKY, Yves

Institut de Mécanique et d'Ingénierie [I2M]

Langue

Article de revue

Ce document a été publié dans

Mechanics of Materials. 2022-10-01, vol. 173, p. 104436

Résumé en anglais

The present work aims at proposing a hybrid physics-AI based model to predict non-linear mechanical behaviors of dissipative materials. By introducing a specific Neural Network architecture called Thermodynamically Consistent Recurrent Neural Networks (ThC-RNN), this study proposes a new paradigm for the simulation of dissipative materials under complex loading conditions. The design of such architecture allows to take into account the material loading history subjected to multi-axial and non-proportional loading paths, similarly to internal variables for homogeneous materials. A special focus has been given to the respect of thermodynamics principles in the ThC-RNN model by introducing specific thermodynamical constraints during the training phase. Finally, the model’s reliability has been tested on different plasticity models once the training is completed. It is shown that thermodynamic consistency improves significantly the prediction capabilities of the ThC-RNN model, considering several outputs such as stress tensor and tangent modulus components, state variables and mechanical work rate partition (recoverable part, irrecoverable part and dissipative part).< Réduire

Mots clés en anglais

Recurrent Neural Network (RNN)

Plasticity

Dissipative materials

Non-proportional loadings

Thermodynamics

Métadonnées

Partager cette publication !

Licence d’utilisation du document

Thermodynamically consistent Recurrent Neural Networks to predict non linear behaviors of dissipative materials subjected to non-proportional loading paths

Langue

Ce document a été publié dans

Résumé en anglais

Mots clés en anglais

URI

DOI

Unités de recherche