Parametric analysis and minimization of entropy generation in bioinspired magnetized non-Newtonian nanofluid pumping using artificial neural networks and particle swarm optimization

Abbas, MA, Beg, OA ORCID: https://orcid.org/0000-0001-5925-6711, Zeeshan, A, Hobiny, A and Bhatti, MM 2021, 'Parametric analysis and minimization of entropy generation in bioinspired magnetized non-Newtonian nanofluid pumping using artificial neural networks and particle swarm optimization' , Thermal Science and Engineering Progress, 24 , p. 100930.

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Abstract

Magnetohydrodynamic rheological bio-inspired pumping systems are finding new applications in modern energy systems. These systems combined the electrically conducting properties of flowing liquids with rheological behaviour, biological geometries and propulsion mechanisms. Further enhancements in transport characteristics can be achieved with the deployment of nanofluids. Second law thermodynamic analysis also provides a useful technique for optimizing thermal performance by minimizing entropy generation. In the present study, all these aspects are combined to analyze the heat transfer in magnetic viscoelastic nanofluid flow in a two-dimensional deformable channel containing a rigid porous matrix under peristaltic waves subject to a transverse magnetic field. The Williamson model is deployed for the nanofluid rheology and the Buongiorno model for nanoscale effects. Under lubrication approximations, the conservation equations for mass, momentum, energy and nanoparticle species are simplified. These partial differential equations are further non-dimensionalized using relevant transformation variables. The mathematical model is solved analytically by means of the Homotopy Analysis Method (HAM). Next, entropy generation is minimized by applying Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN). In the first phase, the equation for Entropy generation is derived as a function of temperature distribution, velocity profile utilizing geometrical and thermophysical parameters. The first step is to discover entropy generation to estimate some extraordinary influencing parameters. In the next step, some specific multi-layer perceptron ANNs are trained, which depend on the information from the first stage. In the last step, PSO in the considered peristaltic flow is used to minimize entropy generation. The optimized value (minimum) of entropy generation is 3.65 kJ/kg acquired at magnetic parameter (M)= 3, Brownian motion parameter (

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Thermal Science and Engineering Progress
Publisher: Elsevier
ISSN: 2451-9049
Related URLs:
Depositing User: OA Beg
Date Deposited: 14 Apr 2021 14:13
Last Modified: 28 Aug 2021 10:53
URI: http://usir.salford.ac.uk/id/eprint/60013

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