TY - JOUR
T1 - Ohmic heating effects and entropy generation for nanofluidic system of Ree-Eyring fluid
T2 - Intelligent computing paradigm
AU - Shoaib, Muhammad
AU - Zubair, Ghania
AU - Nisar, Kottakkaran Sooppy
AU - Raja, Muhammad Asif Zahoor
AU - Khan, Muhammad Ijaz
AU - Gowda, R. J.Punith
AU - Prasannakumara, B. C.
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Entropy generation
KW - Nano fluid
KW - Ohmic heating
KW - Ree-Eyring fluid
UR - https://www.scopus.com/pages/publications/85118499427
U2 - 10.1016/j.icheatmasstransfer.2021.105683
DO - 10.1016/j.icheatmasstransfer.2021.105683
M3 - Article
AN - SCOPUS:85118499427
SN - 0735-1933
VL - 129
JO - International Communications in Heat and Mass Transfer
JF - International Communications in Heat and Mass Transfer
M1 - 105683
ER -