Predictions of temperature distributions on layered metal plates using artificial neural networks

摘要

The temperature distribution influences the amount of energy needed to heat a body. The benefits of using multi-layered metal plates (MMP) are due to the requirement of a regular temperature distribution on the opposite side with one side heated irregularly. The factors that affect the regular distribution of the temperature in such a structure are the thickness of the layers and the materials themselves, since for different materials, heat conduction coefficients, density and specific heat values change. In this study, the main objective is to find a neural network solution for the problem of the non-regular distribution of temperature on the non-heated side of an irregularly heated MMP consisting of two layers of Cu/CrNi and Al/CrNi in order to obtain the optimum thickness levels for the layers. To achieve this aim, the results of the finite elements method (FEM) produced by the program package ANSYS have been used to train and test the network. They are the coefficient of heat conduction (K), specific heat (C), density (D), temperature (T) and layer thickness (L), which are used as the input layer, while the outputs are the maximum, minimum and mean temperature values of the materials. The back propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function have been used in the network. By using the weights of the network, formulations have been given for each output. The network has yielded R~2 values of 0.999 and the mean percent errors are smaller than 0.8 for the training data, while the R~2 values are about 0.999 and the mean percent errors are smaller than 0.7 for the test data. The analysis has been extended for different materials and for the different temperature values that have been applied. The Al/CrNi laminated plate has a lower temperature gradient distribution on the upper (or non-heated) surface due to its lesser heat conductivity compared to the Cu/CrNi steel. The thickness of 8 mm provides the best results among the alloys that have been considered.