Investigation of Three-Dimensional Condensation Film Problem over an Inclined Rotating Disk Using a Nonlinear Autoregressive Exogenous Model

Ali

et al. 2023

IEEE Access

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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.

Section: Introductionmentioning

confidence: 99%

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

Ali

et al. 2023

IEEE Access

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“…In this paper, numerical solutions for fully wetted longitudinal porous heat exchangers with different thermal conductivities are calculated based on the simple concept of artificial intelligence (AI), implemented through the application of neural networks and optimization procedures of meta-heuristic techniques [ 35 , 36 , 37 , 38 , 39 ]. Recently, artificial intelligence-based stochastic techniques have been successfully implemented for various problems in different domains of reaction kinematics [ 40 , 41 ], marine engineering [ 42 ], wireless communication [ 43 ], and fluid dynamics [ 44 , 45 , 46 ]. These applications motivated the authors to design a novel unsupervised technique using the computational approximation ability of layer structure feed-forward ANNs, the global and local optimization of the Tiki-Taka algorithm (TTA), and sequential quadratic programming (SQP).…”

Section: Introductionmentioning

confidence: 99%

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach

Alhakami

Khan

et al. 2022

Entropy

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The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters , such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10−4 to 10−5 and 10−8 to 10−10, respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme’s results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices.

Quantitative Study of Non-Linear Convection Diffusion Equations for a Rotating-Disc Electrode

Alshammari

Jan

et al. 2023

Entropy

Rotating-disc electrodes (RDEs) are favored technologies for analyzing electrochemical processes in electrically charged cells and other revolving machines, such as engines, compressors, gearboxes, and generators. The model is based on the concept of the nonlinear entropy convection-diffusion equations, which are constructed using semi-boundaries as an infinite notion. In this model, the surrogate solutions with different parameter values for the mathematical characterization of non-dimensional OH− and H+ ion concentrations at a rotating-disc electrode (RDE) are investigated using an intelligent hybrid technique by utilizing neural networks (NN) and the Levenberg–Marquardt algorithm (LMA). Reference solutions were calculated using the RK-4 numerical method. Through the training, validation, and testing sampling of reference solutions, the NN-BLMA approximations were recorded. Error histograms, absolute error, curve fitting graphs, and regression graphs validated the NN-BLMA’s resilience and accuracy for the problem. Additionally, the comparison graphs between the reference solution and the NN-BLMA procedure established that our paradigm is reliable and accurate.