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; Zeeshan, A; Hobiny, A; Bhatti, MM

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 (

Citation

Abbas, M., Beg, O., Zeeshan, A., Hobiny, A., & Bhatti, M. (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, 100930. https://doi.org/10.1016/j.tsep.2021.100930

• Journal Article Type: Article
• Acceptance Date: Apr 6, 2021
• Online Publication Date: Apr 20, 2021
• Publication Date: Aug 1, 2021
• Deposit Date: Apr 14, 2021
• Publicly Available Date: Apr 20, 2022
• Journal: Thermal Science and Engineering Progress
• Print ISSN: 2451-9049
• Publisher: Elsevier
• Volume: 24
• Pages: 100930
• DOI: https://doi.org/10.1016/j.tsep.2021.100930
• Publisher URL: https://doi.org/10.1016/j.tsep.2021.100930
• Related Public URLs: https://www.journals.elsevier.com/thermal-science-and-engineering-progress