학술논문

A Computational Study of Magneto-Convective Heat Transfer Over Inclined Surfaces With Thermodiffusion
Document Type
Periodical
Source
IEEE Access Access, IEEE. 11:57046-57070 2023
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Generators
Magnetohydrodynamics
Heating systems
Convection
Sea surface
Ocean temperature
Salinity (geophysical)
Computational analysis
magnetic field
energy generator
nonlinear systems
dynamic parameters
neuro-computing
hybridization
heat transfer
machine learning
Language
ISSN
2169-3536
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.