Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-45469-6 |
| _version_ | 1797630251558240256 |
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| author | Shaik Jakeer H. Thameem Basha Seethi Reddy Reddisekhar Reddy Mohamed Abbas Mohammed S. Alqahtani K. Loganathan A. Vivek Anand |
| author_facet | Shaik Jakeer H. Thameem Basha Seethi Reddy Reddisekhar Reddy Mohamed Abbas Mohammed S. Alqahtani K. Loganathan A. Vivek Anand |
| author_sort | Shaik Jakeer |
| collection | DOAJ |
| description | Abstract 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 $$\left(K={0.0,2.0,4.0}\right)$$ K = 0.0 , 2.0 , 4.0 , vortex viscosity parameter $$\left({R}_{1}={0.5,1.0,1.5}\right)$$ R 1 = 0.5 , 1.0 , 1.5 , electric field parameter $$\left(E={0.0,0.1,0.2}\right)$$ E = 0.0 , 0.1 , 0.2 , thermal relaxation time $$\left(\Lambda ={0.01,0.10,0.20}\right)$$ Λ = 0.01 , 0.10 , 0.20 , heat source/sink parameter, $$\left(Q=-{0.3,0.0,0.3}\right)$$ Q = - 0.3 , 0.0 , 0.3 thermal radiation parameter $$\left(R={0.5,1}.{0,1.5}\right)$$ R = 0.5 , 1 . 0 , 1.5 , temperature ratio parameter $$\left({\theta }_{w}={0.5,1.0,1.5}\right)$$ θ w = 0.5 , 1.0 , 1.5 ,nanoparticle volume fraction $$\left(\phi ={0.00,0.02,0.04}\right)$$ ϕ = 0.00 , 0.02 , 0.04 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. |
| first_indexed | 2024-03-11T11:05:35Z |
| format | Article |
| id | doaj.art-97a70c32a18641d1b8cede3f3c9f112b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T11:05:35Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-97a70c32a18641d1b8cede3f3c9f112b2023-11-12T12:16:02ZengNature PortfolioScientific Reports2045-23222023-11-0113112910.1038/s41598-023-45469-6Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithmShaik Jakeer0H. Thameem Basha1Seethi Reddy Reddisekhar Reddy2Mohamed Abbas3Mohammed S. Alqahtani4K. Loganathan5A. Vivek Anand6 School of Technology, The Apollo UniversityDepartment of Mathematical Sciences, Ulsan National Institute of Science and TechnologyDepartment of Mathematics, Koneru Lakshmaiah Education FoundationElectrical Engineering Department, College of Engineering, King Khalid UniversityRadiological Sciences Department, College of Applied Medical Sciences, King Khalid UniversityDepartment of Mathematics and Statistics, Manipal University JaipurDepartment of Aeronautical Engineering, MLR Institute of TechnologyAbstract 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 $$\left(K={0.0,2.0,4.0}\right)$$ K = 0.0 , 2.0 , 4.0 , vortex viscosity parameter $$\left({R}_{1}={0.5,1.0,1.5}\right)$$ R 1 = 0.5 , 1.0 , 1.5 , electric field parameter $$\left(E={0.0,0.1,0.2}\right)$$ E = 0.0 , 0.1 , 0.2 , thermal relaxation time $$\left(\Lambda ={0.01,0.10,0.20}\right)$$ Λ = 0.01 , 0.10 , 0.20 , heat source/sink parameter, $$\left(Q=-{0.3,0.0,0.3}\right)$$ Q = - 0.3 , 0.0 , 0.3 thermal radiation parameter $$\left(R={0.5,1}.{0,1.5}\right)$$ R = 0.5 , 1 . 0 , 1.5 , temperature ratio parameter $$\left({\theta }_{w}={0.5,1.0,1.5}\right)$$ θ w = 0.5 , 1.0 , 1.5 ,nanoparticle volume fraction $$\left(\phi ={0.00,0.02,0.04}\right)$$ ϕ = 0.00 , 0.02 , 0.04 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.https://doi.org/10.1038/s41598-023-45469-6 |
| spellingShingle | Shaik Jakeer H. Thameem Basha Seethi Reddy Reddisekhar Reddy Mohamed Abbas Mohammed S. Alqahtani K. Loganathan A. Vivek Anand Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-023-45469-6 |
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