INTELLIGENT PREDICTIVE MODELING OF FERROHYDRODYNAMIC FLOW OVER A ROTATING DISC USING NEURAL NETWORKS AND RANDOM FOREST ALGORITHMS

Abstract

The study in this paper explores the dynamic behavior of ferrofluids over a rotating disc and identifies the key parameters including the effect of the thermal Rayleigh number, ferrohydrodynamic effect and volume fraction of nanoparticles on the velocity, temperature and concentration profiles. These intricate relationships were deciphered and accurate predictive models were developed using a novel approach that integrated the New Iterative Method (NIM) with cutting-edge machine learning algorithms such as neural networks and Random Forest models. The results reveal a strong interaction between thermal and hydrodynamic parameters, which indicates that the heat transfer and flow characteristics can be significantly improved by optimizing these parameters. 

Keywords : Ferrofluid Dynamics, Rotating Disc Flow, Heat Transfer Optimization, New Iterative Method (NIM), Machine Learning Predictive Models.