Research on the influence of convector factors on a panel radiator's heat output and total weight with a machine learning algorithm

Abstract

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.