Electroosmotic microchannel flow of blood conveying copper and cupric nanoparticles: Ciliary motion experiencing entropy generation using backpropagated networks

Author:

Sharma Parikshit1,Sharma Bhupendra K.1,Mishra Nidhish K.2,Almohsen Bandar3,Bhatti Muhammad M.45ORCID

Affiliation:

1. Department of Mathematics Birla Institute of Technology and Science, Pilani Pilani Rajasthan India

2. Department of Basic Science College of Science and Theoretical Studies Saudi Electronic University Riyadh Saudi Arabia

3. Department of Mathematics College of Science King Saud University Riyadh Saudi Arabia

4. College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China

5. Material Science Innovation and Modelling (MaSIM) Research Focus Area North‐West University, Mafikeng Campus Mmabatho South Africa

Abstract

AbstractA novel mathematical model for a hybrid (Cu–CuO/blood) Jeffrey nanofluid passing a vertical symmetric microchannel along with an electroosmosis pump is presented. The focuses on the advancement of mathematical modeling techniques, its comprehensive analysis of microfluidic system dynamics, and its potential to inform the optimal design of devices using nanofluids with broad applications in various fields. Arrhenius's law is used to analyze endothermic–exothermic reactions and activation energy. The governing partial differential equations of the fixed frame are transformed into ordinary differential equations of the wave frame using self‐similarity transformations. Low Reynolds number and long‐wavelength approximations helped to find solutions of the equations by applying a suitable BVP solver in MATLAB. The fluid's velocity, temperature, concentration, and electroosmosis properties are studied graphically. Two‐dimensional contour plots of fluid velocity and three‐dimensional surface plots of fluid properties are discussed. Physically significant quantities of mass transfer rate, skin friction coefficient, entropy generation, and heat transfer rate are studied using contour plots. Artificial neural network simulation using Bayesian regularization backpropagation algorithms is analyzed for training state, error histogram, fit, performance, and regression plots. Conclusively, the comprehensive analysis of the fluid dynamics, entropy generation, mass and heat transfer, and in the microchannel, coupled with the successful implementation of artificial neural network simulation, contributes to an improved understanding of the system's behavior. Entropy generation was raised for enhanced Brownian motion number and reduced values of thermophoresis, activation energy, and endothermic–exothermic reaction parameters. This study's results can be used to improve the efficiency and effectiveness of microfluidic devices used in fields as diverse as electrical cooling and medicine delivery.

Funder

King Saud University

Publisher

Wiley

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