Effect of the magnetic field on the heat transfer coefficient of a Fe ₃ O ₄ -water ferrofluid using artificial intelligence and CFD simulation

A ferrofluid is a magnetic fluid which is composed of magnetic nanoparticles with the size of 5-15nm immersed in a base fluid (such as water, oil, etc.). Although the amount of thermal conductivity of the magnetic nanoparticles is lower than that of metallic and metallic oxide nanoparticles, their constructability by magnetic field makes them ideal to be used in heat transfer applications. In this study, the heat transfer coefficient (HTC) of the Fe ₃ O ₄ nanoparticles dispersed in water under constant and alternating magnetic field is investigated by artificial intelligence methods and CFD simulation. Multilayer feed-forward neural network, group method of data handling type neural network, support vector regression model and adaptive neuro-fuzzy inference system are developed to predict the HTC of the Fe ₃ O ₄ -water ferrofluid under magnetic field. Volume fraction of nanoparticle, intensity of the magnetic field, frequency of the magnetic field, Reynolds number and dimensionless distance of the tube are selected as input variables of the networks and the HTC is selected as output variable of the network. The results show that artificial intelligence methods can successfully predict the target with very good accuracy.