Abstract

In current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.

Details

Title
Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling
Author
Anum, Shafiq 1 ; Çolak Andaç Batur 2 ; Sindhu Tabassum Naz 3 ; Al-Mdallal, Qasem M 4 ; Abdeljawad, T 5 

 Nanjing University of Information Science and Technology, School of Mathematics and Statistics, Nanjing, China (GRID:grid.260478.f) 
 Niğde Ömer Halisdemir University, Mechanical Engineering Department, Niğde, Turkey (GRID:grid.412173.2) (ISNI:0000 0001 0700 8038) 
 Quaid- i- Azam University 45320, Department of Statistics, Islamabad, Pakistan (GRID:grid.412621.2) (ISNI:0000 0001 2215 1297) 
 UAE University, Department of Mathematical Sciences, Al-Ain, United Arab Emirates (GRID:grid.43519.3a) (ISNI:0000 0001 2193 6666) 
 Prince Sultan University, Department of Mathematics and General Sciences, Riyadh, Saudi Arabia (GRID:grid.443351.4) (ISNI:0000 0004 0367 6372) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2551799522
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.