Machine learning approach of Casson hybrid nanofluid flow over a heated stretching surface

Gunisetty Ramasekhar; Shalan Alkarni; Nehad Ali Shah; Gunisetty Ramasekhar; Shalan Alkarni; Nehad Ali Shah

doi:10.3934/math.2024912

AIMS Mathematics

2024, Volume 9, Issue 7: 18746-18762. doi: 10.3934/math.2024912

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Machine learning approach of Casson hybrid nanofluid flow over a heated stretching surface

1.
Department of Mathematics, Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous), Nandyal 518501, Andhra Pradesh, India
2.
Department of Mathematics, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
3.
Department of Mechanical Engineering, Sejong University, Seoul 05006, South Korea

Received: 11 March 2024 Revised: 28 April 2024 Accepted: 06 May 2024 Published: 04 June 2024
MSC : 76A05, 76R05

The present investigation focused on the influence of magnetohydrodynamic Gold-Fe₃O₄ hybrid nanofluid flow over a stretching surface in the presence of a porous medium and linear thermal radiation. This article demonstrates a novel method for implementing an intelligent computational solution by using a multilayer perception (MLP) feed-forward back-propagation artificial neural network (ANN) controlled by the Levenberg-Marquard algorithm. We trained, tested, and validated the ANN model using the obtained data. In this model, we used blood as the base fluid along with Gold-Fe₃O₄ nanoparticles. By using the suitable self-similarity variables, the partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs). After that, the dimensionless equations were solved by using the MATLAB solver in the Fehlberg method, such as those involving velocity, energy, skin friction coefficient, heat transfer rates and other variables. The goals of the ANN model included data selection, network construction, network training, and performance assessment using the mean square error indicator. The influence of key factors on fluid transport properties is presented via tables and graphs. The velocity profile decreased for higher values of the magnetic field parameter and we noticed an increasing tendency in the temperature profile. This type of theoretical investigation is a necessary aspect of the biomedical field and many engineering sectors.
- ANN model,
- Casson fluid,
- MHD,
- thermal radiation,
- porous medium,
- hybrid nanofluid
Citation: Gunisetty Ramasekhar, Shalan Alkarni, Nehad Ali Shah. Machine learning approach of Casson hybrid nanofluid flow over a heated stretching surface[J]. AIMS Mathematics, 2024, 9(7): 18746-18762. doi: 10.3934/math.2024912

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Abstract

The present investigation focused on the influence of magnetohydrodynamic Gold-Fe₃O₄ hybrid nanofluid flow over a stretching surface in the presence of a porous medium and linear thermal radiation. This article demonstrates a novel method for implementing an intelligent computational solution by using a multilayer perception (MLP) feed-forward back-propagation artificial neural network (ANN) controlled by the Levenberg-Marquard algorithm. We trained, tested, and validated the ANN model using the obtained data. In this model, we used blood as the base fluid along with Gold-Fe₃O₄ nanoparticles. By using the suitable self-similarity variables, the partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs). After that, the dimensionless equations were solved by using the MATLAB solver in the Fehlberg method, such as those involving velocity, energy, skin friction coefficient, heat transfer rates and other variables. The goals of the ANN model included data selection, network construction, network training, and performance assessment using the mean square error indicator. The influence of key factors on fluid transport properties is presented via tables and graphs. The velocity profile decreased for higher values of the magnetic field parameter and we noticed an increasing tendency in the temperature profile. This type of theoretical investigation is a necessary aspect of the biomedical field and many engineering sectors.

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