The effects of acoustic pressure level on the heat transfer performance of a wavy channel: Calibrated RANS model with machine learning application

Abstract

In high-tech industries, an improvement in heat transfer performance plays an important role. This investigation was carried out computationally to evaluate the heat transfer structure and flow properties of a wavy channel in a turbulent mode, taking into account various working conditions of the system. In this regard, three-dimensional numerical simulations were conducted utilizing a finite volume code and a machine learning-calibrated RANS model. Initially, the findings were evaluated with available experimental data, demonstrating the high precision of the calibrated RANS model (about 99%) in predicting the flow and heat transfer structure. In addition, the simulations were expanded to examine the effects of acoustic power level external sources as an active control method on the performance of heat transfer. Based on the findings, the heat transfer performance increased up to 27.27% due to increasing the flow momentum creating eddy flows, and enhancing the turbulence intensity using an acoustic power level external source.