Ohmic heating effects and entropy generation for nanofluidic system of Ree-Eyring fluid: Intelligent computing paradigm. (December 2021)

• Record Type: Journal Article
• Authors: Shoaib, Muhammad; Zubair, Ghania; Nisar, Kottakkaran Sooppy; Raja, Muhammad Asif Zahoor; Khan, Muhammad Ijaz; Gowda, R.J. Punith; Prasannakumara, B.C.
• Is Part Of: International communications in heat and mass transfer. Volume 129(2021)
• Publication Date: 2021-12
• Subjects: Ohmic heating -- Entropy generation -- Nano fluid -- Ree-Eyring fluid -- Artificial neural networks; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Heat -- Transmission; Mass transfer; Periodicals; 621.4022
• DOI: 10.1016/j.icheatmasstransfer.2021.105683
• Languages: English
• ISSNs: 0735-1933

Abstract

Abstract: In present study, the nano-material flow of Ree-Eyring fluid model (NF-REFM) is examined by utilizing the technique of Levenberg Marquardt with backpropagated neural networks (TLM-BNNs). The flow is examined between two disks and the impacts of porosity and velocity slip are also analyzed. The partial differential equations (PDEs) representing the NF-REFM are transformed into system of ordinary differential equations (ODEs). Homotopy analysis method (HAM) is used to solve the ODEs and interpret the reference dataset for TLM-BNN. This dataset helps to compute the approximated solution of NF-REFM in MATLAB software. Regression analysis, Error histogram and MSE results, validates the performance of TLM-BNN. The flow effects on the velocity profile, temperature distribution and concentration profile are examined for different parameters. The results for entropy generation, Bejan number, Nusselt number, Sherwood number and skin friction coefficient are also discussed in this article.