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Finite Element Numerical Simulation of Free Convection Heat Transfer in a Square Cavity Containing an Inclined Prismatic Obstacle With Machine Learning Optimization
https://orcid.org/0000-0001-7309-6056
https://orcid.org/0000-0002-9168-3825
ABSTRACTThe present work describes a numerical simulation of free convection heat transfer inside a square cavity containing a prismatic obstacle at various angles of inclination. The nondimensional governing equations are discretized by the finite element method and solved in the commercial software “COMSOL Multiphysics 6.1” with appropriate boundary conditions. The effect of prominent parameters on streamline, isotherm contours, and local Nusselt number profiles are depicted graphically. The control parameters are the Prandtl number and Rayleigh number (103 ≤ Ra ≤ 106). The study considers air as the circulating fluid with the Prandtl number, Pr = 0.71. The computations are conducted for the prismatic shape at four different orientations of , and . The inclination angle of the prismatic obstacle is observed to exert a significant role in the distribution of heat and momentum inside the square cavity. Furthermore, neural network approaches are used for optimizing the thermal performance of the system, via Bayesian regularization machine learning analysis and Levenberg–Marquardt algorithms. The study finds applications in solar collectors, fuel cells, and so forth.
Finite Element Numerical Simulation of Free Convection Heat Transfer in a Square Cavity Containing an Inclined Prismatic Obstacle With Machine Learning Optimization
https://orcid.org/0000-0001-7309-6056
https://orcid.org/0000-0002-9168-3825
ABSTRACTThe present work describes a numerical simulation of free convection heat transfer inside a square cavity containing a prismatic obstacle at various angles of inclination. The nondimensional governing equations are discretized by the finite element method and solved in the commercial software “COMSOL Multiphysics 6.1” with appropriate boundary conditions. The effect of prominent parameters on streamline, isotherm contours, and local Nusselt number profiles are depicted graphically. The control parameters are the Prandtl number and Rayleigh number (103 ≤ Ra ≤ 106). The study considers air as the circulating fluid with the Prandtl number, Pr = 0.71. The computations are conducted for the prismatic shape at four different orientations of , and . The inclination angle of the prismatic obstacle is observed to exert a significant role in the distribution of heat and momentum inside the square cavity. Furthermore, neural network approaches are used for optimizing the thermal performance of the system, via Bayesian regularization machine learning analysis and Levenberg–Marquardt algorithms. The study finds applications in solar collectors, fuel cells, and so forth.
Finite Element Numerical Simulation of Free Convection Heat Transfer in a Square Cavity Containing an Inclined Prismatic Obstacle With Machine Learning Optimization
https://orcid.org/0000-0001-7309-6056
https://orcid.org/0000-0002-9168-3825
ABSTRACTThe present work describes a numerical simulation of free convection heat transfer inside a square cavity containing a prismatic obstacle at various angles of inclination. The nondimensional governing equations are discretized by the finite element method and solved in the commercial software “COMSOL Multiphysics 6.1” with appropriate boundary conditions. The effect of prominent parameters on streamline, isotherm contours, and local Nusselt number profiles are depicted graphically. The control parameters are the Prandtl number and Rayleigh number (103 ≤ Ra ≤ 106). The study considers air as the circulating fluid with the Prandtl number, Pr = 0.71. The computations are conducted for the prismatic shape at four different orientations of , and . The inclination angle of the prismatic obstacle is observed to exert a significant role in the distribution of heat and momentum inside the square cavity. Furthermore, neural network approaches are used for optimizing the thermal performance of the system, via Bayesian regularization machine learning analysis and Levenberg–Marquardt algorithms. The study finds applications in solar collectors, fuel cells, and so forth.
Finite Element Numerical Simulation of Free Convection Heat Transfer in a Square Cavity Containing an Inclined Prismatic Obstacle With Machine Learning Optimization
Heat Trans
Rajarajeswari, Perepi (author) / Arasukumar, Thilagavathi (author) / Anwar Bég, O. (author) / Bég, Tasveer A. (author) / Kuharat, S. (author) / Bala Anki Reddy, P. (author) / Ramachandra Prasad, V. (author)
2025-02-26
Article (Journal)
Electronic Resource
English
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