Heat transfer and flow analysis for square tube with oscillating electromagnetic field with experimental data by artificial neural network

Abstract

The heat transfer and friction factor of ferrofluid passing through a square tube with an oscillating electromagnetic field were investigated experimentally. The impact of electromagnetic rotating direction, flux, and frequency on heat and flow characteristics has been studied. The Brownian motion of particles has been shown to considerably influence the direction, power, and frequency of electromagnetic rotation, resulting in higher heat transfer. The interruption of electromagnetic flow raises the friction factor even further. In addition, a three-layer back propagation network model is built, with input, hidden, and output layers numbered 5, 17, and 2, respectively. This artificial neural network model performed well statistically, with correlation coefficients ranging from 0.99939 to 0.9996 and mean square errors ranging from 0.0106 to 0.0190. The artificial neural network results match the observed data within ±5% and ±10% error ranges for heat and flow characteristics, respectively. Consequently, this machine learning approach might be used to forecast heat exchanger thermal performance.