Design Optimization of Pin Fin Geometry Using Particle Swarm Optimization Algorithm

Abstract

Particle swarm optimization (PSO) is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters.

Modeling and Optimization of Gaseous Thermal Slip Flow in Rectangular Microducts Using a Particle Swarm Optimization Algorithm

“…, andŷ k (t) represent the position at time t, the velocity at time t, the best personal position (p best ) at time t, and the global best position (g best ) of population at time t respectively, whereas c 1 and c 2 are the learning factors of (p best ) in interval between 0 and 2 and r 1 and r 2 are the random numbers in the interval 0 to 1. It is important to note that the other particles follow the performance of the best particle [31][32][33][34]. In addition, each particle keeps the best position that it has achieved so far.…”

“…It is important to note that the other particles follow the performance of the best particle [31][32][33][34]. In addition, each particle keeps the best position that it has achieved so far.…”

Modeling and Optimization of Gaseous Slip Flow Forced Convection in Rectangular Microducts Using Particle Swarm Optimization Algorithm

“…respectively [31]. When the best particle, which has the best performance, is known, the other particles will follow him [32][33][34][35]. In addition, each particle keeps the best position that it has achieved so far.…”

Computing single step operators of logic programming in radial basis function neural networks

“…In PSO, the number of hidden neurons will be indirectly evolved due to the velocity parameter v of the PSO algorithm [11,13,16].…”

“…Many optimization problems have been solved by using metaheuristic algorithms, such as Particle swarm optimization (PSO) and genetic algorithm (GA) [11,12]. PSO algorithm is one of the most widely used algorithms to find the optimal values in order to optimize the expectation as a function [11,13].…”