Mathematical modeling of thermal contact resistance for different curvature contacting geometries using a robust approach

Nowadays, researchers have become interested in acquiring deeper knowledge concerning Thermal Contact Conductance (TCC) and Thermal Contact Resistance (TCR) existing among various types of metals during heat transfer occurrence in the nuclear reactor, thermal control system of spacecraft, and heat exchangers. In the present study, Artificial Neural Network (ANN) coupled with Multi-Layer Perceptron (MLP) modeling was utilized to predict transient temperature contour on various contacting surfaces such as flat-flat, flat-cylinder, and cylinder-cylinder. In order to develop an accurate transient model, the parameters of metals including position, time, and roughness were used as input parameters, and temperatures of solid bodies were selected as the target parameter of the model. Modeling results demonstrate that ANN-based modeling outperforms other numerical methods in terms of accuracy. Moreover, values of Average Absolute Relative Deviation (AARD) and coefficient of determination (R-2) for the overall data are 0.056 and 0.996, respectively, which prove the accuracy and robustness of the proposed model. (C) 2019 Sharif University of Technology. All rights reserved.