Prediction of optimum design variables for maximum heat transfer through a rectangular porous fin using particle swarm optimization

Deshamukhya, Tuhin; Bhanja, Dipankar; Nath, Sujit; Hazarika, Saheera Azmi

doi:10.1007/s12206-018-0846-9

Cited by 19 publications

(14 citation statements)

References 21 publications

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“…As Ra increases, the buoyant forces become dominant which improves actual heat transfer rate which improves the convective heat transfer rate. As noticed in Figure 4(a), increase in Rayleigh number decreases the efficiency by increasing the ideal heat transfer rate at a higher rate as expressed in equation (31). Porosity results in removal of solid material which lowers down the efficiency due to the drop in effective thermal conductivity.…”

Section: Effect Of Different Parameters On the Efficiencymentioning

confidence: 91%

“…The optimization results revealed that optimum heat transfer rate with two branches increases with the increase in fin volume up to a certain volume after which it decreases continuously. Deshamukhya et al 31 carried out an analysis on short porous fins with convective tip end, where they optimized the dominant variables using PSO. The analysis was carried out for three specific-tip temperatures.…”

Section: Introductionmentioning

confidence: 99%

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

Deshamukhya

Bhanja

Nath

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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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.

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Section: Effect Of Different Parameters On the Efficiencymentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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

Deshamukhya

Bhanja

Nath

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy [45], [46], inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques.…”

Section: Sensor Deployment Algorithmmentioning

confidence: 99%

Sensor Deployment Method Based on Faiw-DPSO in DASNs

et al. 2020

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Distributed Active Sensing Networks (DASNs) is a new sensing paradigm, where active and passive sensors are distributed in a field, and collaboratively detect the objects. The detectability is the most important property of DASNs. ''Exposure'' is defined to quantify the dimension limitations in detectability. Thus, it is important to deploy the sensors with the minimum exposure to improve the detectability. To minimize exposure is NP-hard, thus it is necessary to solve it by heuristic algorithms. In this paper, we present a Discrete Particle Swarm Optimization (DPSO)-based solution to achieve the minimum exposure. Furthermore, a feedback-based adjustment on the inertia weight of DPSO is designed to improve the convergence speed and global searching ability of algorithm. By a large number of simulations, this improved DPSO (Faiw-DPSO) is proved to outperform greedy algorithm by up to 74% and perform better than other related algorithms. This algorithm is robust, efficient and self-adaptive in regular and irregular monitoring field. INDEX TERMS Distributed active sensing networks (DASNs), discrete particle swarm optimization (DPSO), exposure, feedback-based adjustment, inertia weight.

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“…Deshamukhya et al. 52 introduced the idea of applying swarm intelligence in the area of porous fins in the year 2018. They used PSO to optimize 4 important variables of short porous fins at three known tip condition values in order to maximize the heat transfer rate.…”

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

Bhanja

Nath

2020

Proceedings of the Institution of Mechanical Engineers, Part A:

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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.

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Prediction of optimum design variables for maximum heat transfer through a rectangular porous fin using particle swarm optimization

Cited by 19 publications

References 21 publications

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

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

Sensor Deployment Method Based on Faiw-DPSO in DASNs

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

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