Analytical study of temperature distribution in a rectangular porous fin considering both insulated and convective tip

Proceedings of the Institution of Mechanical Engineers, Part C:

2020

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The high-end devices, sophisticated gadgets, smart home appliances, superfast vehicles and aero engines of twenty-first century demand better heat removal for efficient functioning. Thus, with an aim to facilitate higher heat transfer from mechanical and electronic equipment, extensive research throughout the world has been going at a steady pace. One such achievement in the field of heat transfer is the introduction of the porous fins. This paper aims to discuss the various important aspects of porous fins along with the noteworthy research and developments in this field over the last decade. The review starts with a brief idea of heat transfer mechanism through porous media followed by some discussion on the thermal analysis of porous fins. Usually, the complicated nonlinear energy equations of porous fins require analytical techniques along with numerical analysis to solve the same. A good number of relevant research works have been cited to understand the optimum shaping and derivation of governing differential equations. Finally, a strengths, weakness, opportunities and threats analysis has been done to analyze the various aspects of using a porous fin.

Section: Thermal Analysis Using Analytical Techniquesmentioning

confidence: 99%

Section: Thermal Analysis Using Analytical Techniquesmentioning

confidence: 99%

Heat transfer enhancement through porous fins: A comprehensive review of recent developments and innovations

Deshamukhya¹,

Proceedings of the Institution of Mechanical Engineers, Part C:

2020

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“…Recently, Deshamukhya et al. 25 carried out an analysis to investigate the temperature distribution in a rectangular porous fin using ADM considering both insulated as well as convective tip conditions. Das and Ooi 26 also studied porous fins under different tip conditions.…”

Section: Introductionmentioning

confidence: 99%

Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions

Deshamukhya

Proceedings of the Institution of Mechanical Engineers, Part A:

2020

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In this novel optimization analysis, a rectangular porous fin subjected to convective tip conditions along with insulated end condition is studied. The aim is to optimize the important variables responsible for transferring heat from fin with insulated as well as convective tip to the surrounding in order to get a higher rate of heat transfer from the fin surface. Temperature-dependent solid and fluid thermal conductivities as well as variable internal heat generation is considered. To obtain heat dissipation rate through porous fin, the nonlinear governing equation is first solved analytically using Adomian decomposition method. Then the heat transfer rate, i.e., objective function of this problem is optimized with three powerful metaheuristic techniques, namely firefly algorithm, particle swarm optimization and gravitational search algorithm. A comparative analysis has revealed that fins with convective end show significantly higher heat transfer rate compared to their insulated end counterparts. Particle swarm optimization showed marginally higher heat transfer values compared to firefly algorithm but firefly algorithm took less computational effort to reach the optimum value.

“…The authors reported a scheme to improve the heat transfer rate in porous T-shaped fins as compared to their solid counterparts by judicial selection of parameter combination, especially porosity. Deshamukhya et al 12 in a rectangular porous fin considered a case, where the fin transfers heat through convection, and studied the temperature distribution considering both convective as well as insulated tip conditions. In the year that followed, Deshamukhya et al 13 carried out a complicated study, where they considered the variation of individual thermal conductivities (of solid material and fluid) in order to determine the efficiency of rectangular porous fins considering both insulated as well as convective tip conditions.…”

Section: Introductionmentioning

confidence: 99%

Optimization of constructal T-shaped porous fins under convective environment using Swarm Intelligence Algorithms

Deshamukhya

Proceedings of the Institution of Mechanical Engineers, Part G:

2020

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Constructal T-shaped porous fins transfer better heat compared to the rectangular counterparts by improving the heat flow through the low resistive links. This type of fins can be used in aerospace engines which demand faster removal of heat without adding extra weight of the overall assembly. Here, in this study, three powerful nature-inspired metaheuristic algorithms such as particle swarm optimization, gravitational search algorithm, and Firefly algorithm have been used to optimize the dominant thermo physical as well as geometric parameters which are responsible for transferring heat at faster rates from the fin body satisfying a volume constraint. The temperature distribution along the stem and the flange has been plotted, and the effect of important parameters on the efficiency has been determined. Three different volumes are selected for the analysis, and the results have shown marked improvement in the optimized heat transfer rate. Particle swarm optimization has reported an increase of 0.81%, while Firefly algorithm reports 0.83% improvement as we increase the fin volume from 500 to 1000 and 0.19% (by PSO) and 0.4% (by FA) as the volume increases from 1000 to 1500. The paper also presents a scheme of reducing the computational effort required by the algorithms to converge around the optimum point. While a reduction of 14.36% computational effort has been achieved in particle swarm optimization’s convergence time, Firefly algorithm took 24.64% less time to converge at the near-optimum point. While particle swarm optimization has converged at better optimal points compared to Firefly algorithm and Gravitational search algorithm, Gravitational search algorithm has outperformed the two algorithms in terms of computational time. Gravitational search algorithm took 61.72 and 29.33% less time to converge as compared to particle swarm optimization and Firefly algorithm, respectively.