Analyzing tube arrangements of a finned-tube heat exchanger to optimize overall efficiency using artificial neural network and response surface methodology

Temperature is a critical factor in numerous equipment, industries, and daily life applications. Heat exchangers are essential devices that help regulate and optimize this factor. The finned-tube heat exchanger (FTHE) is widely favored due to its high efficiency in facilitating heat transfer between liquids and gases. Improving the performance of FTHE can significantly provide thermal requirements in industrial and engineering processes and reduce energy consumption. The present research includes a numerical study of an FTHE and the optimization of the performances based on the input variables by response surface methodology (RSM) and artificial neural network (ANN) methods. The tubes' transverse and longitudinal pitches and inlet Reynolds number were selected as input variables. Also, the examined responses were the Colburn and friction factors. Changing tubes' pitches makes it possible to significantly affect the thermo-hydraulic performance without incurring additional costs and processes. The acquired results showed that the responses predicted by the models are very close to the numerical results, which indicates the high accuracy and validity of these models. According to the results, the optimum heat exchanger's efficiency index was obtained at P_t = 26.128 mm, P_l = 28 mm, and Re = 800. It was also observed that the overall performance of the optimal design is 185 % higher than the weakest FTHE.