Artificial neural network approach for investigating the impact of convector design parameters on the heat transfer and total weight of panel radiators

Abstract

The difficulty of the production stages of panel radiators used for heating purposes reveals the importance of determining the heat transfer performance and panel radiator weight values, which are determined depending on the design parameters. In the present work, an artificial neural network model is proposed for predicting the heat transfer and weight values of a panel radiator as outputs depending on the design parameters of convectors. In the multilayer network model developed with 78 numerically obtained data sets, 8 different design parameters were defined as input parameters and heat transfer and in the output layer panel weight values were obtained. The design parameters of the convectors, in other words, input parameters of network model were chosen as the height of convector, thickness of convector sheet, the trapezoidal height of convector, convector base length, opposing convector distance, tip width of convector, convector vertical location and distance between convectors. For the proposed neural network model, the mean squared errors obtained for the heat transfer and panel radiator weight are −1.25E-04 and -7.54E-05 respectively. In addition, an R-value of 0.99999 has been obtained, and the average deviation value has been calculated as 0.001%. The obtained results show that, depending on the design parameters, the proposed artificial neural network model can predict the rate of heat transfer and weight of the panel radiator with high accuracy. This investigation is supposed to fill a significant gap since it is the pioneer one in open sources on machine learning modeling of panel radiators. Thus, it can possibly make a crucial contribution to the related manufacturing industry.