Factor Analysis of Convective Heat Transfer for a Horizontal Tube in the Turbulent Flow Region Using Artificial Neural Network

Abstract Artificial neural network (ANN) has shown its superior predictive power compared to the conventional approaches in many studies. However, it is always treated as a ‘black box ’ because it provides little explanation to the relative influence of the independent variables in the prediction process. Ghajar et al. [1] used the ANN method to develop an empirical correlation for the heat transfer data in a horizontal tube with a reentrant inlet under uniform wall heat flux boundary condition in the transition region. In their work, the least and the most important variables were examined using the coefficient matrices based on a single training. However, the method was only applied to one set of experimental data. The applicability of their method to other data sets is not known. In this study, the method proposed in the previous study is modified and a new set of experimental data for different inlet configurations (square-edged and bell-mouth) from the work of Ghajar and Tam [2] in the turbulent region are used to further verify this method. An index of contribution is defined in this study. Furthermore, the gradient method used and the number of neurons and iterations for each training are carefully examined. Using the revised method and the index of contribution defined in this study, an ANN correlation is established and the Reynolds number (Re) and the Prandtl number (Pr) are observed as the most important parameters. The length-to-diameter ratio (x/D) and the viscosity ratio (µb/µw)0.14 are found to be the least important parameters