https://doi.org/10.1140/epjp/s13360-022-03583-w
Regular Article
Modeling and simulation of Maxwell nanofluid flows in the presence of Lorentz and Darcy–Forchheimer forces: toward a new approach on Buongiorno’s model using artificial neural network (ANN)
1
Department of Mathematics, Abdul Wali Khan University Mardan, 23200, Mardan, Khyber Pakhtunkhwa, Pakistan
2
Department of Computing and Technology, Abasyn University, 25000, Peshawar, Pakistan
3
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, 64002, Douliou, Yunlin, Taiwan, ROC
Received:
14
August
2022
Accepted:
9
December
2022
Published online:
1
February
2023
The current work explores the intelligent computational strength of neural networks based on the Levenberg–Marquardt backpropagation (LMBP-NNs) neural networks technique for simulation of Maxwell nanofluid flow past a linear stretchable surface model. The fluid flow is incorporated Rosseland’s thermal radiation, and Darcy’s Forchheimer law. The Maxwell nanofluid model gives more relaxing time to momentum boundary layer. For the nanofluid phenomena that concentrate on thermophoresis and Brownian motion, Buongiorno’s model is used. The procedure transforms partial differential equations arising in nanofluidics systems with an appropriate degree of similarity into nonlinear differential equation systems. For the nonlinear nanofluid problem with accuracy having order 4–5, the (FDM) finite difference method (Lobatto IIIA) is implemented via various selections of collocation points. The strong aspect of Lobatto IIIA is its ability to handle very nonlinear couple differential equations in an easy manner. The precise results of (FDM) are used to build the reference datasets for LMBP-NNs technique for the various factors of fluid problem. The design scheme for various factors of fluid problem carries out a series of operations based on training, testing, and authentication on reference dataset. The accuracy of LMBP-NNs is checked through statistical based neural network tools such that mean square error, regression plot, curve fitting graphs, and error histogram. Furthermore, the investigation of flow model parameters for momentum, energy, and concentration profiles is described via visual representation.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
