Eur. Phys. J. Appl. Phys.
Volume 93, Number 1, January 2021
|Number of page(s)||14|
|Section||Physics of Energy Transfer, Conversion and Storage|
|Published online||06 January 2021|
A novel ANN-based method for simultaneous estimation of thermophysical properties using experimental photothermal data
Processes, Materials and Solar Energy (PROMES-CNRS, UPR 8521), Tecnosud, Rambla de la Thermodynamique, Perpignan 66100, France
2 Processes, Materials and Solar Energy (PROMES-CNRS, UPR 8521), 7 rue du Four Solaire, Font-Romeu-Odeillo-Via 66120, France
3 University of Perpignan Via Domitia, 52 Avenue Paul Alduy, Perpignan 66860, France
* e-mail: email@example.com
Received in final form: 14 October 2020
Accepted: 13 November 2020
Published online: 6 January 2021
In this paper, a novel artificial neural network (ANN) based method dedicated to simultaneously estimating thermal conductivity and thermal diffusivity of CSP (concentrating solar power) plant receiver materials is presented. By monitoring the evolution of these two correlated thermophysical properties during aging cycles, CSP plants' cost efficiency could be maintained. The proposed method is based on the processing of experimental photothermal data using classification and estimation networks. All the networks are feedforward ANN trained with supervised learning algorithms. A pseudo random binary signal (PRBS) is used as excitation and the impact on performance of both the photothermal response length, which is used as model input, and the number of training examples has been evaluated. Of course, the networks' topology has been optimized, allowing the generalization ability to be controlled. Despite the lack of data, the results are promising. Mean relative errors are between 8% and 20%, and the main levers for improvement are identified. In this paper, the study deals with a large range of materials (polymers and metallic alloys).
© R. Reoyo-Prats et al., EDP Sciences, 2021
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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