A modified firefly algorithm to maximize heat dissipation of a rectangular porous fin in heat exchangers exposed to both convective and radiative environment

2019 ◽  
Vol 233 (6) ◽  
pp. 1203-1216 ◽  
Author(s):  
Tuhin Deshamukhya ◽  
Ratnadeep Nath ◽  
Saheera Azmi Hazarika ◽  
Dipankar Bhanja ◽  
Sujit Nath
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Particle Swarm ◽  
Transformation Method ◽  
Differential Transformation Method ◽  
Computational Time ◽  
Swarm Optimization ◽  
Differential Transformation ◽  
Porous Fins

The current study deals with the optimization of significant parameters of aluminium and copper rectangular porous fins using firefly algorithm with reflective boundary condition. The study has been done considering convective heat transfer, in the first case, as well as combined convective and radiative modes of heat transfer, in the second case. To solve the non-linear governing equation, a semi analytical technique, differential transformation method is adopted. The results obtained by differential transformation method are validated by the numerical solution obtained by the finite difference method. The performance of firefly algorithm is evaluated by comparing with the results obtained by particle swarm optimization where it is seen that for the current set of equations, firefly algorithm took lesser number of iterations and computational time to converge than particle swarm optimization for all the cases. The analysis has been done for three different fin volumes and the effect of important variables which directly influence the heat transfer rate through porous fins has been discussed.

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

2020 ◽  
Vol 234 (14) ◽  
pp. 2100-2113
Author(s):  
Tuhin Deshamukhya ◽  
Dipankar Bhanja ◽  
Sujit Nath
Keyword(s):  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Computational Effort ◽  
Swarm Optimization ◽  
Optimum Point ◽  
Gravitational Search ◽  
Porous Fins

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.

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

2020 ◽  
Vol 234 (8) ◽  
pp. 1175-1188
Author(s):  
Tuhin Deshamukhya ◽  
Dipankar Bhanja ◽  
Sujit Nath
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Firefly Algorithm ◽  
Particle Swarm ◽  
Thermal Design ◽  
Swarm Optimization ◽  
Porous Fin ◽  
Adomian Decomposition

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.

Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun-Yung Chen ◽  
Te-Wen Tu
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Hollow Cylinder ◽  
General Type ◽  
Particle Swarm ◽  
Time History ◽  
Time Varying ◽  
Swarm Optimization ◽  
Self Learning

Abstract An inverse methodology is proposed to estimate a time-varying heat transfer coefficient (HTC) for a hollow cylinder with time-dependent boundary conditions of different kinds on inner and outer surfaces. The temperatures at both the inner surface and the interior domain are measured for the hollow cylinder, while the time history of HTC of the outer surface will be inversely determined. This work first expressed the unknown function of HTC in a general form with unknown coefficients, and then regarded these unknown coefficients as the estimated parameters which can be randomly searched and found by the self-learning particle swarm optimization (SLPSO) method. The objective function which wants to be minimized was found with the absolute errors between the measured and estimated temperatures at several measurement times. If the objective function converges toward the null, the inverse solution of the estimated HTC will be found eventually. From numerical experiments, when the function of HTC with exponential type is performed, the unknown coefficients of the HTC function can be accurately estimated. On the contrary, when the function of HTC with a general type is conducted, the unknown coefficients of HTC are poorly estimated. However, the estimated coefficients of an HTC function with the general type can be regarded as the equivalent coefficients for the real function of HTC.

scholarly journals DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS

Irriga ◽  
2018 ◽  
Vol 23 (4) ◽  
pp. 798-817
Author(s):  
Saulo de Tarso Marques Bezerra ◽  
José Eloim Silva de Macêdo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Water Distribution ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computational Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Irrigation Network

DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS     SAULO DE TARSO MARQUES BEZERRA1 E JOSÉ ELOIM SILVA DE MACÊDO2   1 Universidade Federal de Pernambuco, Campus Agreste, Núcleo de Tecnologia, Avenida Campina Grande, S/N, Bairro Nova Caruaru, CEP 55014-900, Caruaru, Pernambuco, Brasil. [email protected]. 2 Centro Universitário Maurício de Nassau, Departamento de Engenharia Civil, BR 104, Km 68, S/N, Bairro Agamenon Magalhães, CEP 55000-000, Caruaru, Pernambuco, Brasil. [email protected].     1 RESUMO   Apresenta-se, neste trabalho, um modelo de otimização para o dimensionamento de sistemas pressurizados de distribuição de água para projetos de irrigação. A metodologia empregada é fundamentada no algoritmo Otimização por Enxame de Partículas (PSO), que é inspirada na dinâmica e comportamento social observados em muitas espécies de pássaros, insetos e cardumes de peixes. O PSO proposto foi aplicado em dois benchmark problems reportados na literatura, que correspondem à Hanoi network e a um sistema de irrigação localizado na Espanha. O dimensionamento resultou, para as mesmas condições de contorno, na solução de ótimo global para a Hanoi network, enquanto a aplicação do PSO na Balerma irrigation network demonstrou que o método proposto foi capaz de encontrar soluções quase ótimas para um sistema de grande porte com um tempo computacional razoável.   Palavras-chave: água, irrigação, análise econômica.     BEZERRA, S. T. M.; MACÊDO, J. E. S. LOOPED WATER DISTRIBUTION NETWORKS DESIGN VIA PARTICLE SWARM OPTIMIZATION ALGORITHM     2 ABSTRACT   This paper presents an optimization model for the design of pressurized water distribution systems for irrigation projects. The methodology is based on the Particle Swarm Optimization algorithm (PSO), which is inspired by the social foraging behavior of some animals such as flocking behavior of birds and the schooling behavior of fish. The proposed PSO has been tested on two benchmark problems reported in the literature, which correspond to the Hanoi network and an irrigation system located in Spain. The design resulted in the global optimum for the Hanoi network, while the application of PSO in Balerma irrigation network demonstrated that the proposed method was able to find almost optimal solutions for a large-scale network with reasonable computational time.   Keywords: water, irrigation, economic analysis. O desempenho do método foi comparado com trabalhos prévios, demonstrando convergência rápida e resultados satisfatórios na busca da solução ótima de um sistema com elevado exigência computacional.

scholarly journals Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

2021 ◽  
pp. 242-249
Author(s):  
M.Shahkhir Mozamir ◽  
◽  
Rohani Binti Abu Bakar ◽  
Wan Isni Soffiah Wan Din ◽  
Zalili Binti Musa
Keyword(s):  
Particle Swarm Optimization ◽  
Indoor Localization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Computational Time ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Local Neighborhood

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

Sign in / Sign up

Export Citation Format

Share Document