Parameter Identification for a Multivariable Nonlinear Constitutive Model inside ANSYS Workbench

Parameter Identification for a Multivariable Nonlinear Constitutive Model inside ANSYS Workbench

Stať ve sborníku v databázi WoS či Scopus

This contribution aims to describe the process of the inverse identification of the parameters of a nonlinear material model from experimentally obtained data. This process takes place with the aid of approaches utilizing optimization procedures based on population methods which are currently implemented in the ANSYS Workbench environment. The input data for the described numerical procedure took the form of the points of a load-displacement curve which was measured during the performance of a three-point bending test on a concrete beam. This experiment was numerically simulated via the finite element method with the use of the Menétrey-Willam nonlinear material model. Great attention is paid to the description of the sensitivity analysis and the parameter correlation performed with the utilization of a programmed script that enables the correct understanding of the used material model. Emphasis is therefore placed on the analysis of individual parameters whose understanding and correct setting have a significant influence on the convergence of the nonlinear solution. The basic principle of the identification by optimization is the minimization of the difference between experimentally and numerically obtained load-displacement curves. However, the problem is how to formulate this difference as precise as possible because the right choice of objective function is crucial for achieving the optimum. One possible way is to use the root-mean-squared error that is often used for evaluation of accuracy of economy or weather mathematical models. The text also deals with the possibility of a reduction in the design vector according to the results of sensitivity analysis and shows how this reduction affects the accuracy of the sought parameters. The contribution provides another view on the utilization of optimization algorithms in the area of the design of safe and effective structures.

This contribution aims to describe the process of the inverse identification of the parameters of a nonlinear material model from experimentally obtained data. This process takes place with the aid of approaches utilizing optimization procedures based on population methods which are currently implemented in the ANSYS Workbench environment. The input data for the described numerical procedure took the form of the points of a load-displacement curve which was measured during the performance of a three-point bending test on a concrete beam. This experiment was numerically simulated via the finite element method with the use of the Menétrey-Willam nonlinear material model. Great attention is paid to the description of the sensitivity analysis and the parameter correlation performed with the utilization of a programmed script that enables the correct understanding of the used material model. Emphasis is therefore placed on the analysis of individual parameters whose understanding and correct setting have a significant influence on the convergence of the nonlinear solution. The basic principle of the identification by optimization is the minimization of the difference between experimentally and numerically obtained load-displacement curves. However, the problem is how to formulate this difference as precise as possible because the right choice of objective function is crucial for achieving the optimum. One possible way is to use the root-mean-squared error that is often used for evaluation of accuracy of economy or weather mathematical models. The text also deals with the possibility of a reduction in the design vector according to the results of sensitivity analysis and shows how this reduction affects the accuracy of the sought parameters. The contribution provides another view on the utilization of optimization algorithms in the area of the design of safe and effective structures.

Optimization; ANSYS Workbench; sensitivity; nonlinear material model; root-mean-squared error; identification.

Optimization; ANSYS Workbench; sensitivity; nonlinear material model; root-mean-squared error; identification.

HOKEŠ, F.; KALA, J.; HUŠEK, M.; KRÁL, P.

2017

13.06.2016

Elsevier

Praha, Česká republika

World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium, WMCAUS 2016

1877-7058

Procedia Engineering

161

1

Spojené království Velké Británie a Severního Irska

892

897

6

https://www.sciencedirect.com/science/article/pii/S1877705816329721

http://hdl.handle.net/11012/204216

1-s2.0-S1877705816329721-main