Numerical Investigation and Machine Learning-Based Prediction of the Effect of Using Ring Turbulators on Heat Transfer Characteristics in a Counterflow Heat Exchanger

Abstract

Pipe-type heat exchangers are commonly used in industrial applications to facilitate heat transfer between two fluids at different temperatures without mixing them. In this study, turbulators were employed in a counterflow concentric pipe-type heat exchanger. Water at a flow rate of 50 l/h and a temperature of 298.14 K, and air at a temperature of 350 K were directed through the inner pipe. The different stages of circular turbulators placed inside the inner pipe were numerically investigated using the feasible κ-ε turbulence model. Heat transfer characteristics were examined for a turbulator-free heat exchanger and for turbulator-heat exchanger models with helical turbulators of 25, 50, 75, and 100 mm pitch at Reynolds numbers ranging from 4000 to 26000. The governing equations for three-dimensional and turbulent flow conditions in a steady state were solved using a computational fluid dynamics program based on the finite volume method. Temperature distributions and velocity contours in the heat exchanger were generated using the data obtained from numerical analysis. Additionally, predictions were made using artificial neural networks. The results revealed that the highest enhancement in heat transfer, amounting to 233.08% compared to the empty tube case, was achieved with the 25 mm pitch turbulator. The predictions made using artificial neural networks were in good agreement with the numerical analysis results. The designed turbulators for the heat exchanger model promoted turbulent flow, increased the heat transfer area, and led to an improvement in heat transfer.