Issue |
Mechanics & Industry
Volume 24, 2023
|
|
---|---|---|
Article Number | 4 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.1051/meca/2022030 | |
Published online | 25 January 2023 |
Estimation of thermophysic properties of a bimaterial by temperature field measurement and finite element model updating
1
Université de Lomé, Faculté des Sciences, Département de Physique,
BP 1515,
Lome, Togo
2
Institut Pprime, UPR 3346 CNRS, Université de Poitiers, ENSMA SP2MI,
BP 30179,
86962
Futuroscope Chasseneuil Cedex, France
* e-mail: jean.christophe.dupre@univ-poitiers.fr
Received:
8
November
2022
Accepted:
20
December
2022
The aim of this study is to identify simultaneously the thermal conductivity tensor and the heat capacity per unit volume of a bimaterial, whose heat conduction obeys Fourier’s law. This approach is validated by numerical simulation. The simulated temperature fields are obtained by the direct resolution of the heat conduction equation solved numerically with the help of finite element method formulation. To identify the parameters, an inverse method is developed by using the finite element model updating (FEMU) based on the Levenberg-Marquardt algorithm. This inverse finite element method approach allowed us to estimate the thermophysical parameters sought. We validated the numerical procedure by using noiseless temperature fields at different time and space steps and two types of material: an homogeneous and a bimaterial one. To be close to real conditions, the influence of the noise on the temperature fields is also studied and shows the efficiency of the inverse method. The results of this procedure show that the identified parameters are very less sensitive to the number of infra-red images varying from 40 to 80 and the number of elements ranging from 20 to 50 for a specimen size equals to 36.6 × 36.6 mm2.
Key words: Bimaterial / heat transfer / finite elements / thermal conductivity tensor / heat capacity / inverse problem
© Y. Koumekpo et al., Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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