Heat transfer and fluid flow for tube included a porous media: Assessment and Multi-Objective Optimization Using Particle Swarm Optimization (PSO) Algorithm

Keykhah, Sajjad; Assareh, Ehsanolah; Moltames, Rahim; Izadi, Mohsen; Ali, Hafız Muhammad

doi:10.1016/j.physa.2019.123804

Cited by 24 publications

(3 citation statements)

References 31 publications

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“…There are several efforts that have been made to solve the problem of path planning with various constraints and characteristics. The most exploited metaheuristic algorithms for solving this problem are Genetic Algorithm (GA) (Lim et al, 2020), Particle Swarm Optimization (PSO) (Keykhah et al, 2020), Ant Colony Optimization (ACO) (Uthayakumar et al, 2020), and Artificial Bee Colony (ABC) (Deng et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

Abdel‐Basset

Eldrandaly

Shawky

et al. 2022

Sustainable Cities and Society

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Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre -including this research content -immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

Abdel‐Basset

Eldrandaly

Shawky

et al. 2022

Sustainable Cities and Society

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“…The PSO is an efficient algorithm for feature selection areas because the method is faster to converge and easier to implement when compared to other meta heuristic algorithms [21]. Additionally, the PSO is one of the most important meta heuristic algorithms in the field of collective intelligence [22]. A detailed description of the PSO technique is described in detail in Section 3.…”

Section: Literature Reviewmentioning

confidence: 99%

A Heuristic Simulation–Optimization Approach to Information Sharing in Supply Chains

Park

2020

Symmetry

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The sustainability of the supply chain is possible only if the profitability of all the tiers participating in that supply chain is guaranteed. The profitability of each of these tiers is ensured if information sharing as well as an effective and seamless coordination system are realized between the tiers. This process reduces the influence of an important risk factor known as the bullwhip effect. The purpose of the current study is to determine the necessary information sharing level to optimize the supply chain that has asymmetric flows of input and output values and to examine the effects of information sharing on the order fill rate (OFR) and total inventory cost (TIC) of the supply chain through analysis of variance (ANOVA) testing. In this work, the supply chain was optimized by using the particle swarm optimization (PSO) technique, with an objective function that assumes the maximization of OFR and minimization of TIC. The proposed method showed excellent results in comparing the mean, variance, and coefficient of variation. In addition, the method used the ANOVA test with a 5% significance level to verify the impact of the information sharing level.

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“…The Regression analysis is used to generate a mathematical model of output response as a function of input response. The mathematical model can be optimized using various optimization approaches as Genetic_algorithm, simulated_annealing, and Particle swirm optimization [42][43][44][45] etc. In this paper, experimental investigation of influence of Reynolds number, pulsating frequency, and input heat power source on turbulent flow heat transfer through pipe.…”

Section: Introductionmentioning

confidence: 99%

Heat Transfer Features of Pulsating Turbulent Flow in a Pipe: Experimental Optimization

Nishandar¹,

Pise²,

Mhamane³

2023

Advances in Computer Science Research

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The heat transmission improvement methods are widely utilized to raise the level of performance of the latent heat storage systems, the heat pipe is a broadly used technique utilized to improve the transmission of heat enhancement technique. Current paper comprises use of the heat pipe to vary the heat input, pulsating frequency, and flow velocity. The Heat input is varied in 0, 25, 50, 75 and 100 watts; pulsating frequency in upstream and downstream with frequency changed from 0 to 3.33 Hz; Flow velocity varied and calculated from Reynolds number which varies from 6000 to 13000. The experimental results were carried out using the design of experiments with output parameters as coefficient of heat transfer along with Nusselt number. The behavior of coefficient of heat transfer with respect to input parameters is discussed. The Analysis of variance is accustomed to check the reliability of the outcomes. The optimized input response selected using Particle swirm optimization shows Pulsating frequency of 3.33 Hz, Heat input of 100 W and Reynolds number of 13443 giving optimum heat transfer coefficient.

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Heat transfer and fluid flow for tube included a porous media: Assessment and Multi-Objective Optimization Using Particle Swarm Optimization (PSO) Algorithm

Cited by 24 publications

References 31 publications

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

A Heuristic Simulation–Optimization Approach to Information Sharing in Supply Chains

Heat Transfer Features of Pulsating Turbulent Flow in a Pipe: Experimental Optimization

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