Enhancement of heat transfer of confined enclosures with free convection using blocks with PSO algorithm

Azimifar, A.; Payan, S.

doi:10.1016/j.applthermaleng.2015.11.122

Cited by 21 publications

(7 citation statements)

References 31 publications

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“…In the engineering field, the applications are as diverse as possible. Optimization problems involving PSO can be found in the literature in order to increase the heat transfer of systems [16] or even in algorithms to predict the heat transfer coefficient [17]. In the field of thermodynamics, one can find papers involving the optimization of thermal systems such as diesel engine-organic Rankine cycle [18], hybrid diesel-ORC/photovoltaic system [19], and integrated solar combined cycle power plants (ISCCs) [20].…”

Section: Recent Applications and Challengesmentioning

confidence: 99%

Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

Almeida¹,

Leite²

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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This chapter will introduce the particle swarm optimization (PSO) algorithm giving an overview of it. In order to formally present the mathematical formulation of PSO algorithm, the classical version will be used, that is, the inertial version; meanwhile, PSO variants will be summarized. Besides that, hybrid methods representing a combination of heuristic and deterministic optimization methods are going to be presented as well. Before the presentation of these algorithms, the reader will be introduced to the main challenges when approaching PSO algorithm. Two study cases of diverse nature, one regarding the PSO in its classical version and another one regarding the hybrid version, are provided in this chapter showing how handful and versatile it is to work with PSO. The former case is the optimization of a mechanical structure in the nuclear fuel bundle and the last case is the optimization of the cost function of a cogeneration system using PSO in a hybrid optimization. Finally, a conclusion is presented.

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Section: Recent Applications and Challengesmentioning

confidence: 99%

Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

Almeida¹,

Leite²

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

View full text Add to dashboard Cite

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“…In their work, the range of Rayleigh numbers was between 10 4 and 10 6 in their work. The prospect of increasing the heat transfer through the confined cavities by applying geometric change based on the concept of highly conductive solid blocks was studied (Azimifar and Payan, 2016). In this study, the confined cavity was examined with three sets of boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous optimization of inclination angle and fin characteristics on heat transfer in a rectangular enclosure

Payan

Nada²,

Sarhaddi³

2021

JER is an international, peer-reviewed journal that publishes f

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This paper examines the prospect of increasing or decreasing the heat transfer through a rectangular enclosure with an aspect ratio of 2 in two Rayleigh numbers 105 and 106 by optimizing its inclination angle and the parameters of a highly conductive thin fin attached to its hot surface. The enclosure is heated from below, and the dominant heat transfer mechanism within the enclosure is the free convection. Optimization is performed by particle swarm optimization algorithm. The equations of energy, continuity, and momentum for free convection heat transfer in the enclosure are discretized by the finite volume method and are numerically solved. The optimization objective is to adjust the enclosure inclination angle and the fin parameters (position and height), so that heat transfer into the cold wall becomes minimized or maximized. The attained results reveal that the heat transfer through such an enclosure can be increased significantly up to 23% by its inclination angle 20.6° and the addition of a thin fin at the optimal location 0.72 and optimal height 0.2 in Rayleigh 106. Also, the obtained results show that heat transfer can be decreased up to 32% by the slight adjustment of its inclination angle 1.7° and addition of a thin fin at the optimal location 1.41 and optimal height 0.2 in Rayleigh number 105.

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“…At present, the most commonly used task scheduling algorithm in cloud work-flow management system is proposed by Eberhart and Shi [1]. It uses a random optimization heuristic algorithm established by group intelligence [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…It uses a random optimization heuristic algorithm established by group intelligence [1,2]. PSO algorithm has the characteristics of few parameters, easy implement and fast convergence.…”

Section: Introductionmentioning

confidence: 99%

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Research on Cloud Computing Task Scheduling based on Improved Particle Swarm Optimization

Zhao¹

2017

IJPE

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Particle swarm optimization (PSO) is a popular intelligent algorithm to solve the task scheduling optimization problem of work-flow system in cloud computing environment. However, this algorithm is easy to fall into the local optimality. It is the reason that the execution time and cost of the scheduling scheme are higher than other methods. Therefore, by improving the calculation method of the single particle success value, the traditional adaptive inertia weight particle group task scheduling algorithm is optimized. Through each particle fitness and local optimal value and global optimal value that divided into four cases to compare, the inertia weight improved can be used to adjust the particle velocity more accurately. It can better equilibrate search capacity of particles between global and local, and avoid the local maximum of the particles. In this paper, we more accurately describe the particle state and improve the inertia weight. We can get a scheduling scheme with lower execution time and lower cost. The analog results show that the improved algorithm is stable. The convergence accuracy is obviously improved. It can effectively avoid prematurely falling into the local optimality.

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Enhancement of heat transfer of confined enclosures with free convection using blocks with PSO algorithm

Cited by 21 publications

References 31 publications

Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

Simultaneous optimization of inclination angle and fin characteristics on heat transfer in a rectangular enclosure

Research on Cloud Computing Task Scheduling based on Improved Particle Swarm Optimization

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