Research output: Contribution to journal › Article › peer-review
Parameter identification in elasto-plasticity : distance between parameters and impact of measurement errors. / Shutov, Alexey V.; Kaygorodtseva, Anastasia A.
In: ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik, Vol. 99, No. 8, 201800340, 08.2019.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Parameter identification in elasto-plasticity
T2 - distance between parameters and impact of measurement errors
AU - Shutov, Alexey V.
AU - Kaygorodtseva, Anastasia A.
PY - 2019/8
Y1 - 2019/8
N2 - A special aspect of parameter identification in finite-strain elasto-plasticity is considered. Namely, we analyze the impact of the measurement errors on the resulting set of material parameters. In order to define the sensitivity of parameters with respect to the measurement errors, a mechanics-based distance between two sets of parameters is introduced. Using this distance function, we assess the reliability of certain parameter identification procedures. The assessment involves introduction of artificial noise to the experimental data; the noise can be both correlated and uncorrelated. An analytical procedure to speed up Monte Carlo simulations is presented. As a result, a simple tool for estimating the robustness of parameter identification is obtained. The efficiency of the approach is illustrated using a model of finite-strain viscoplasticity, which accounts for combined isotropic and kinematic hardening. It is shown that dealing with correlated measurement errors, most stable identification results are obtained for non-diagonal weighting matrix. At the same time, there is a conflict between the stability and accuracy.
AB - A special aspect of parameter identification in finite-strain elasto-plasticity is considered. Namely, we analyze the impact of the measurement errors on the resulting set of material parameters. In order to define the sensitivity of parameters with respect to the measurement errors, a mechanics-based distance between two sets of parameters is introduced. Using this distance function, we assess the reliability of certain parameter identification procedures. The assessment involves introduction of artificial noise to the experimental data; the noise can be both correlated and uncorrelated. An analytical procedure to speed up Monte Carlo simulations is presented. As a result, a simple tool for estimating the robustness of parameter identification is obtained. The efficiency of the approach is illustrated using a model of finite-strain viscoplasticity, which accounts for combined isotropic and kinematic hardening. It is shown that dealing with correlated measurement errors, most stable identification results are obtained for non-diagonal weighting matrix. At the same time, there is a conflict between the stability and accuracy.
KW - 74C15
KW - 74D10
KW - 74E10
KW - 74P10
KW - distance between parameters
KW - finite strain
KW - isotropic and kinematic hardening
KW - measurement error
KW - parameter identification
KW - viscoplasticity
KW - CALIBRATION
KW - BEHAVIOR
KW - FINITE-STRAIN VISCOPLASTICITY
KW - SENSITIVITY
KW - SIMULATION
KW - VIRTUAL FIELDS METHOD
KW - ELEMENTS
KW - INTEGRATION
KW - INELASTIC CONSTITUTIVE MODELS
UR - http://www.scopus.com/inward/record.url?scp=85066464215&partnerID=8YFLogxK
U2 - 10.1002/zamm.201800340
DO - 10.1002/zamm.201800340
M3 - Article
AN - SCOPUS:85066464215
VL - 99
JO - ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik
JF - ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik
SN - 0044-2267
IS - 8
M1 - 201800340
ER -
ID: 20343333