https://doi.org/10.1140/epjp/s13360-022-03622-6
Regular Article
Research on the influence of convector factors on a panel radiator’s heat output and total weight with a machine learning algorithm
1
Mechanical Engineering Department, Engineering Faculty, Gazi University, 06570, Ankara, Turkey
2
Information Technologies Application and Research Center, Istanbul Commerce University, 34445, Istanbul, Turkey
3
Department of Mechanical Engineering, Eastern Mediterranean University, G. Magosa, TRNC, Mersin 10, Turkey
4
Department of Mechanical Engineering, Mechanical Engineering Faculty, Yildiz Technical University, 34349, Istanbul, Turkey
Received:
9
September
2022
Accepted:
21
December
2022
Published online:
18
January
2023
In the current work, the impacts of convector factors of a panel radiator regarding heat output and total weight have been investigated using a machine learning algorithm. An artificial neural network model, widely evaluated by machine learning algorithms, has been created to determine the heat output and total weight values of panel radiators. There are 10 neurons in the hidden layer of the machine learning model, which was trained using 111 numerically obtained data sets. A comprehensive numerical investigation has been done for dissimilar geometrical dimensions of convectors evaluated in panel radiators and validated with experimental results. Afterward, the Levenberg–Marquardt structure has been employed as the training one in the multilayer perceptron network structure. The heat output and total weight outcomes acquired from the artificial neural network have been contrasted with the computational data and the compatibility of the data has been examined comprehensively. Furthermore, various performance parameters have also been determined and the estimation performance of the neural network has been examined thoroughly. The mean deviation values for the thermal power and weight values gained from the network structure have been determined as 0.04 and 0.004%, in turn, and the R-value has been obtained as 0.99999. The investigation outcomes indicated that the proposed neural network can forecast the heat output and total weight values of the panel radiator with very high accuracy.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
