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Research Article | Open Access

Intelligent neural networks approach for analysis of the MHD viscous nanofluid flow due to rotating disk with slip effect

Department of Mathematics, College of Science, University of Ha'il, Ha'il 2440, Saudi Arabia; Email: y.jawarneh@uoh.edu.sa
Department of Basic Sciences, General Administration of Preparatory Year, King Faisal University, P.O. Box 400, Al Ahsa 31982, Saudi Arabia
Department of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box 400, Al Ahsa 31982, Saudi Arabia
Department of Mathematics, Government Postgraduate College Nowshera, Khyber Pakhtunkhwa, Pakistan, Email: ajedakbar@gmail.com, rafimaths957@gmail.com
PAAET, College of Technological Studies, Laboratory Technology Department, Shuwaikh 70654, Kuwait; Email: As.zada@paaet.edu.kw
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Abstract

This analysis used the backpropagation Levenberg-Marquardt technique coupled through neural networks (BPLMT-NN). The magnetohydrodynamic (MHD) viscous nanofluid flow due to the rotating disk (MHD-VNRD) through the slip effect was investigated. In the existence of a velocity slip condition, this communication investigated the boundary coating flow of viscous nanofluid under MHD conditions. The flow was produced by a disk that was revolving. A fluid effects electricity under the effect of a magnetic field that is transverse. The magnetic field that is generated is neglected when the magnetic Reynolds number is low. The properties of Brownian and thermophoresis motion were demonstrated using a nanofluid simulation. Hypotheses about the boundary coating and low magnetic Reynolds number were made while formulating the problem. To transform nonlinear partial differential equations into a scheme of ordinary differential equations, the similarity transformation was utilized. On the profiles of velocity, temperature, and concentration, a data set for the suggested (BPLMT-NN) was created for the impacts of several important parameters and was illustrated via the explicit Runge-Kutta technique. Using the (BPLMT-NN) testing, training, and validation approach, the estimated result of various situations was endorsed, and the suggested model was evaluated for fitness. After that, the proposed (BPLMT-NN) was validated using mean square error (MSE), regression analysis, and histogram investigations. The novelty of the proposed BPLMT-NN technique has various applications, such as disease diagnosis, robotic control systems, ecosystem evaluation, etc. We conducted analyses of some statistical data like gradient, performance, and epoch of the proposed fluid model. Based on closeness, as well as recommended and reference results, the suggested approach has made a distinction with precision level varying from 10 09 to 10 11 .

CLC number: 34G20, 35R11

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AIMS Mathematics
Pages 10387-10412

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Cite this article:
Jawarneh Y, Noor S, Akbar A, et al. Intelligent neural networks approach for analysis of the MHD viscous nanofluid flow due to rotating disk with slip effect. AIMS Mathematics, 2025, 10(5): 10387-10412. https://doi.org/10.3934/math.2025473

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Received: 02 January 2025
Revised: 03 April 2025
Accepted: 15 April 2025
Published: 15 May 2025
©2025 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)