Optimisation of design parameters of the finned tube heat exchanger by numerical simulations and artificial neural networks for the condensing wall hang boilers

Abstract

This research investigates the use of computational fluid dynamics (CFD) and artificial neural networks (ANNs) to optimise the design of finned tube heat exchangers for use in condensing wall-mounted boilers (WHBcs). Fin height, thickness, and distance are selected as the input design parameters, and the internal volume of the heat engine is modelled using the CFDHT (CFD and heat transfer) method. Different ANN structures are trained and tested on the resulting data to identify the optimal training process. The trained ANN is then used to predict various output parameters, including total heat transfer on the inner surface of the tube, maximum temperature on the fins, total heat transfer per unit volume of the heat exchanger, and pressure drop between the inlet and outlet of the internal volume. The optimal design scenarios are evaluated based on design criteria, and the ANN is found to have good statistical performance, with an average accuracy of 1.00018 and a maximum relative error of 9.16%. The ANN is able to accurately estimate the optimal design case.