Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 15 April 2018 / Approved: 16 April 2018 / Online: 16 April 2018 (10:44:40 CEST)

How to cite: Khan, W.A.; Hamadneh, N.N. Modeling and Optimization of Gaseous Slip Flow Forced Convection in Rectangular Microducts Using Particle Swarm Optimization Algorithm. Preprints 2018, 2018040205 (doi: 10.20944/preprints201804.0205.v1). Khan, W.A.; Hamadneh, N.N. Modeling and Optimization of Gaseous Slip Flow Forced Convection in Rectangular Microducts Using Particle Swarm Optimization Algorithm. Preprints 2018, 2018040205 (doi: 10.20944/preprints201804.0205.v1).

Abstract

In this study, pressure driven gaseous slip flow is investigated in microducts of rectangular cross-section. The range of Knudsen numbers Kn in the flow regime is taken as 0.001 ≤ Kn ≤ 0.1 and aspect ratio is taken as 0 ≤ ε ≤ 1. To incorporate rarefaction effects, the effects of slip velocity and temperature jump boundary conditions are taken in to account. The dimensionless momentum and energy equations are solved using MATLAB to obtain the dimensionless velocity and temperature gradients for different values of Knudsen numbers and aspect ratio. Using these gradients, the dimensionless shear stress and heat transfer rate are obtained numerically. The numerical solution can be validated for the special cases when there is no slip (continuum flow), ε = 0 (parallel plates) and ε = 1 (square microducts). An artificial neural network is used to develop separate models for dimensionless shear stress and heat transfer rate and particle swarm optimization algorithm is used to obtain optimum values for both parameters. Using these results, minimum dimensionless shear stress and maximum heat transfer rate can be determined in the microducts under consideration in the slip flow regime. The optimal values of P0 and Nu are found when ε = 1 and Kn = 0.001.

Subject Areas

slip flow; microducts; forced convection; Poiseuille number; Nusselt number; artificial neural network; particle swarm optimization

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