A Computational Study of Magneto-Convective Heat Transfer Over Inclined Surfaces With Thermodiffusion

Abstract

In this article, the ocean energy generator system is analysed. The need for sustainable renewable energy systems is continually growing, given the situation of the world's energy supplies. Numerous similar systems, including photovoltaic solar collectors, biomass, and wind turbines are used for energy generation. The ocean energy generator system uses the magnetohydrodynamics transformation concept to convert kinetic energy to electrical energy. Similar to conventional generators, an ocean generator requires an applied magnetic field to generate current, making it a critical component of the system. To optimize the performance and efficiency of ocean generators, various devices utilizing superconducting magnets have been developed, including Hall current generators, rotating channels, rotating disc magnetohydrodynamics generators, and helicoid generators. However, these systems also involve complex heat, momentum, and mass transfer, which can be better understood through mathematical modeling. The similarity transformation are introduced to transform the mathematical model from partial differential equation system to ordinary differential equation system. By adopting this approach, the numerical solution is significantly simplified while still preserving numerous crucial physical aspects of the studied heat and material transport phenomena. The physical characteristics of sea waves are governed by the three variables of seawater: temperature, salinity, and pressure. Small dispersed particles also affect the generation of hydroelectric power from surface water. The behaviour of velocity, temperature, and salinity profile is observed for the variations of different parameters such as magnetic, Grashof number and heat source. The system is converted into an optimization problem and solved by a neural network procedure. The solutions are compared with reference solutions for validation. The errors, performance, testing and training data are also presented graphically. The data is typically visualized using histograms, line graphs, and other visual aids. This allows for easier comprehension and analysis of the data.

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“…[37] another interesting study entitled mathematical modeling for water and oil saturation during secondary recovery. A computational work of Magneto-convective heat transfers past inclined surfaces under thermos diffusion was contributed by Khan et al [38]. In order to analyze the carbon capture in forest plantations Sulaiman and his contributing researchers [39] added a stochastic NARX neural network and in ref.…”