Heat transfer enhancement of serpentine channels with twisted tape insert by computational fluid dynamics and artificial intelligence

Abstract

Improving the thermal–hydraulic performance of heat exchangers is a critical challenge. In this study, we investigated the impact of twisted tape insert geometrical variables, namely pitch length (P) and diameter (W), on the Nusselt number (Nu) and friction factor (f) within serpentine channels. To accomplish this, we employed computational fluid dynamics (CFD) as our simulation technique, with water as the working fluid. The validity of the CFD data was confirmed, leading to the development of artificial intelligence (AI) subsets: artificial neural network (ANN) and genetic algorithm (GA). In the prediction models, the following input variables were considered: Reynolds number (Re), (P/Dh), and (W/Dh). The ANN and GA models achieved a mean relative error (MRE) of 0.125% and 1.326% for Nu, and 0.139% and 4.104% for f, respectively. These results demonstrate the high accuracy of the correlations, with the ANN model showcasing superior performance.