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Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm

The purpose of this paper is to analyze the heat transfer behavior of the electromagnetic 3D micropolar tri-hybrid nanofluid flow of a solar radiative slendering sheet with non-Fourier heat flux model. The conversion of solar radiation into thermal energy is an area of significant interest as the de...

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Autores principales: Jakeer, Shaik, Basha, H. Thameem, Reddy, Seethi Reddy Reddisekhar, Abbas, Mohamed, Alqahtani, Mohammed S., Loganathan, K., Anand, A. Vivek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628236/
https://www.ncbi.nlm.nih.gov/pubmed/37932305
http://dx.doi.org/10.1038/s41598-023-45469-6
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author Jakeer, Shaik
Basha, H. Thameem
Reddy, Seethi Reddy Reddisekhar
Abbas, Mohamed
Alqahtani, Mohammed S.
Loganathan, K.
Anand, A. Vivek
author_facet Jakeer, Shaik
Basha, H. Thameem
Reddy, Seethi Reddy Reddisekhar
Abbas, Mohamed
Alqahtani, Mohammed S.
Loganathan, K.
Anand, A. Vivek
author_sort Jakeer, Shaik
collection PubMed
description The purpose of this paper is to analyze the heat transfer behavior of the electromagnetic 3D micropolar tri-hybrid nanofluid flow of a solar radiative slendering sheet with non-Fourier heat flux model. The conversion of solar radiation into thermal energy is an area of significant interest as the demand for renewable heat and power continues to grow. Due to their enhanced ability to promote heat transmission, nanofluids can significantly contribute to enhancing the efficiency of solar-thermal systems. The combination of silicon oil-based silicon (Si), magnesium oxide (MgO), and titanium (Ti) nanofluids has attracted attention for their ability to improve the performance of solar-thermal systems. The present study discloses a new approach for intelligent numerical computing solving, which utilizes an MLP feed-forward back-propagation ANN and the Levenberg-Marquard algorithm. The collection of data was conducted for the purpose of testing, certifying, and training the ANN model. The Bvp4c solver in MATLAB is utilized to solve the nonlinear equations governing the momentum, temperature, skin-friction coefficient, and Nusselt number. The characteristics of numerous dimensionless parameters such as porosity parameter [Formula: see text] , vortex viscosity parameter [Formula: see text] , electric field parameter [Formula: see text] , thermal relaxation time [Formula: see text] , heat source/sink parameter, [Formula: see text] thermal radiation parameter [Formula: see text] , temperature ratio parameter [Formula: see text] ,nanoparticle volume fraction [Formula: see text] on Si + MgO + Ti/silicon oil micropolar tri-hybrid nanofluida are analyzed. The ANN model engages in a process of data selection, network construction, training, and evaluation of its effectiveness through the utilization of mean square error. Tables and graphs are used to show how essential parameters affect fluid transport properties. The velocity profile is decreased by higher values of the porosity parameter, whereas the temperature profile is increased. The temperature profile is inversely proportional to higher values of the electric field parameter. The micro-rotation profiles reduced by expanding values vortex viscosity parameter. It has been determined that entropy generation and Bejan number intensifications for enlarged nanoparticle volume fraction.
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spelling pubmed-106282362023-11-08 Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm Jakeer, Shaik Basha, H. Thameem Reddy, Seethi Reddy Reddisekhar Abbas, Mohamed Alqahtani, Mohammed S. Loganathan, K. Anand, A. Vivek Sci Rep Article The purpose of this paper is to analyze the heat transfer behavior of the electromagnetic 3D micropolar tri-hybrid nanofluid flow of a solar radiative slendering sheet with non-Fourier heat flux model. The conversion of solar radiation into thermal energy is an area of significant interest as the demand for renewable heat and power continues to grow. Due to their enhanced ability to promote heat transmission, nanofluids can significantly contribute to enhancing the efficiency of solar-thermal systems. The combination of silicon oil-based silicon (Si), magnesium oxide (MgO), and titanium (Ti) nanofluids has attracted attention for their ability to improve the performance of solar-thermal systems. The present study discloses a new approach for intelligent numerical computing solving, which utilizes an MLP feed-forward back-propagation ANN and the Levenberg-Marquard algorithm. The collection of data was conducted for the purpose of testing, certifying, and training the ANN model. The Bvp4c solver in MATLAB is utilized to solve the nonlinear equations governing the momentum, temperature, skin-friction coefficient, and Nusselt number. The characteristics of numerous dimensionless parameters such as porosity parameter [Formula: see text] , vortex viscosity parameter [Formula: see text] , electric field parameter [Formula: see text] , thermal relaxation time [Formula: see text] , heat source/sink parameter, [Formula: see text] thermal radiation parameter [Formula: see text] , temperature ratio parameter [Formula: see text] ,nanoparticle volume fraction [Formula: see text] on Si + MgO + Ti/silicon oil micropolar tri-hybrid nanofluida are analyzed. The ANN model engages in a process of data selection, network construction, training, and evaluation of its effectiveness through the utilization of mean square error. Tables and graphs are used to show how essential parameters affect fluid transport properties. The velocity profile is decreased by higher values of the porosity parameter, whereas the temperature profile is increased. The temperature profile is inversely proportional to higher values of the electric field parameter. The micro-rotation profiles reduced by expanding values vortex viscosity parameter. It has been determined that entropy generation and Bejan number intensifications for enlarged nanoparticle volume fraction. Nature Publishing Group UK 2023-11-06 /pmc/articles/PMC10628236/ /pubmed/37932305 http://dx.doi.org/10.1038/s41598-023-45469-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jakeer, Shaik
Basha, H. Thameem
Reddy, Seethi Reddy Reddisekhar
Abbas, Mohamed
Alqahtani, Mohammed S.
Loganathan, K.
Anand, A. Vivek
Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
title Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
title_full Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
title_fullStr Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
title_full_unstemmed Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
title_short Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
title_sort entropy analysis on emhd 3d micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628236/
https://www.ncbi.nlm.nih.gov/pubmed/37932305
http://dx.doi.org/10.1038/s41598-023-45469-6
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