Machine learning approach on chemical species and thermal management in Ellis’s fluid considering hall currents and thermal jumps across sphere via Taguchi analysis

The schemed article explores the robust application of machine learning (ML) approach to investigate the chemical species demeanor along with thermal management toward the Ellis’s Fluid, specifically focusing on the characteristics of Hall current together with thermal jumps within a sphere. The fundamental focus relies on the understanding of how these considered factors affects thermal transportation as well as chemical reactions across the fluid. A comprehensive technique is utilized, invoking Taguchi analysis in order to optimize the constraints affecting the performance of the system, like fluid properties, external magnetic field along with thermal conductivity. The contemplated machine learning (ML) model is disciplined to predict the chemical as well as thermal behavior exposed to diverse factors. Under these terms, insights within the optimal configurations specified by efficacious thermal management and also species distribution is provided. The outcomes illustrate the machine learning potential in improving the design as well as systems optimization considering magnetohydrodynamic and also thermal transportation offering a novel technique for dealing complex challenges of fluid dynamics within the engineering applications. Further, the outcomes are summarized and showcased exemplary outcomes through graphical format and also shown in tabular form for clarity.