BAYESIAN INFERENCE OF HETEROGENEOUS VISCOPLASTIC MATERIAL PARAMETERS

Eliška Janouchová, Anna Kučerová

Abstract


Modelling of heterogeneous materials based on randomness of model input parameters involves parameter identification which is focused on solving a stochastic inversion problem. It can be formulated as a search for probabilistic description of model parameters providing the distribution of the model response corresponding to the distribution of the observed data

In this contribution, a numerical model of kinematic and isotropic hardening for viscoplastic material is calibrated on a basis of experimental data from a cyclic loading test at a high temperature. Five material model parameters are identified in probabilistic setting. The core of the identification method is the Bayesian inference of uncertain statistical moments of a prescribed joint lognormal distribution of the parameters. At first, synthetic experimental data are used to verify the identification procedure, then the real experimental data are processed to calibrate the material model of copper alloy.


Keywords


parameter identification, Bayesian inference, hierarchical modelling, aleatory uncertainty, kinematic and isotropic hardening

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This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN 2336-5382 (Online)
Published by the Czech Technical University in Prague