BAYESIAN INFERENCE OF HETEROGENEOUS VISCOPLASTIC MATERIAL PARAMETERS

Autor: Eliška Janouchová, Anna Kučerová
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Acta Polytechnica CTU Proceedings, Vol 15, Iss 0, Pp 41-45 (2018)
Druh dokumentu: article
ISSN: 2336-5382
DOI: 10.14311/APP.2018.15.0041
Popis: 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 dataIn 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.
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