Journal
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
Volume 9, Issue 6, Pages 14372-14387Publisher
ELSEVIER
DOI: 10.1016/j.jmrt.2020.09.098
Keywords
Eyring powell; Magneto-nanofluid; Intelligent numerical computing; Neural network; Levenberg-Marquard scheme; Unsteady 3D flow
Funding
- National Natural Science Foundation of China [11971142, 11871202, 61673169, 11701176, 11626101, 11601485]
Ask authors/readers for more resources
In the presented study, a novel application of intelligent numerical computing solver based on neural networks backpropagated with the Levenberg-Marquard scheme (NN-BLMS) is presented to interpret the chemical reactions and activation energy in unsteady 3D flow of Eyring-Powell magneto-nanofluidic system for convective heat and mass flux scenarios. The original nonlinear coupled PDEs representing the Eyring-Powell magneto-nanofluidic model (EPMNM) is transformed to an equivalent nonlinear ODEs system by exploiting similarity variables. A dataset for the proposed NN-BLMS is generated for different scenarios of EPMNM by variation of radiation, temperature ratio parameter, heat generation, Brownian motion and thermophoresis parameters by using Adam numerical method. The training, testing, and validation processes of NN-BLMS are performed to determine the approximate solution of EPMNM for different cases and comparison with reference results to verify the correctness of the proposed NN-BLMS. The performance of the proposed NN-BLMS to effectively solve the EPMNM is endorsed through mean squared error, histogram studies and regression analysis. The close matching of the proposed and reference results based on error analysis form level 10(-05) to 10(-07) validates the correctness of the proposed methodology. (C) 2020 The Author(s). Published by Elsevier B.V.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available