Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency
Abstract This article investigates solar energy storage due to the Jeffrey hybrid nanofluid flow containing gyrotactic microorganisms through a porous medium for parabolic trough solar collectors. The mechanism of thermophoresis and Brownian motion for the graphene and silver nanoparticles are also...
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| Format: | Article |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88877-6 |
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| author | Anup Kumar Bhupendra K. Sharma Bandar Almohsen Laura M. Pérez Kamil Urbanowicz |
| author_facet | Anup Kumar Bhupendra K. Sharma Bandar Almohsen Laura M. Pérez Kamil Urbanowicz |
| author_sort | Anup Kumar |
| collection | DOAJ |
| description | Abstract This article investigates solar energy storage due to the Jeffrey hybrid nanofluid flow containing gyrotactic microorganisms through a porous medium for parabolic trough solar collectors. The mechanism of thermophoresis and Brownian motion for the graphene and silver nanoparticles are also encountered in the suspension of water-based heat transfer fluid. The gyrotactic microorganisms have the ability to move in an upward direction in the nanofluid mixture, which enhances the nanoparticle stability and fluid mixing in the suspension. Mathematical modeling of the governing equations uses the conservation principles of mass, momentum, energy, concentration, and microorganism concentration. The non-similar variables are introduced to the dimensional governing equations to get the non-dimensional ordinary differential equations. The Cash and Carp method is implemented to solve the non-dimensional equations. The artificial neural network is also developed for the non-dimensional governing equations using the Levenberg Marquardt algorithm. Numerical findings corresponding to the diverse parameters influencing the nanofluid flow and heat transfer are presented in the graphs. The thermal profiles are observed to be enhanced with the escalation in the Darcy and Forchheimer parameters. And the Nusselt number enhances with the escalation in the Deborah number and retardation time parameter. Entropy generation reduces with an enhancement in Deborah number and retardation time parameter. Solar energy is the best renewable energy source. It can fulfill the energy requirements for the growth of industries and engineering applications. |
| format | Article |
| id | doaj-art-225b736c0861484998cb2993f7ab0335 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-225b736c0861484998cb2993f7ab03352025-02-09T12:31:22ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-88877-6Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiencyAnup Kumar0Bhupendra K. Sharma1Bandar Almohsen2Laura M. Pérez3Kamil Urbanowicz4Department of Mathematics, Birla Institute of Technology and Science PilaniDepartment of Mathematics, Birla Institute of Technology and Science PilaniDepartment of Mathematics, College of Science, King Saud UniversityDepartamento de Ingeniería Industrial y de Sistemas, Universidad de TarapacáFaculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology in SzczecinAbstract This article investigates solar energy storage due to the Jeffrey hybrid nanofluid flow containing gyrotactic microorganisms through a porous medium for parabolic trough solar collectors. The mechanism of thermophoresis and Brownian motion for the graphene and silver nanoparticles are also encountered in the suspension of water-based heat transfer fluid. The gyrotactic microorganisms have the ability to move in an upward direction in the nanofluid mixture, which enhances the nanoparticle stability and fluid mixing in the suspension. Mathematical modeling of the governing equations uses the conservation principles of mass, momentum, energy, concentration, and microorganism concentration. The non-similar variables are introduced to the dimensional governing equations to get the non-dimensional ordinary differential equations. The Cash and Carp method is implemented to solve the non-dimensional equations. The artificial neural network is also developed for the non-dimensional governing equations using the Levenberg Marquardt algorithm. Numerical findings corresponding to the diverse parameters influencing the nanofluid flow and heat transfer are presented in the graphs. The thermal profiles are observed to be enhanced with the escalation in the Darcy and Forchheimer parameters. And the Nusselt number enhances with the escalation in the Deborah number and retardation time parameter. Entropy generation reduces with an enhancement in Deborah number and retardation time parameter. Solar energy is the best renewable energy source. It can fulfill the energy requirements for the growth of industries and engineering applications.https://doi.org/10.1038/s41598-025-88877-6Solar energyGraphene and Silver nanoparticlesGyrotactic microorganismsElectro-magneto-hydrodynamic |
| spellingShingle | Anup Kumar Bhupendra K. Sharma Bandar Almohsen Laura M. Pérez Kamil Urbanowicz Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency Scientific Reports Solar energy Graphene and Silver nanoparticles Gyrotactic microorganisms Electro-magneto-hydrodynamic |
| title | Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency |
| title_full | Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency |
| title_fullStr | Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency |
| title_full_unstemmed | Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency |
| title_short | Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency |
| title_sort | artificial neural network analysis of jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency |
| topic | Solar energy Graphene and Silver nanoparticles Gyrotactic microorganisms Electro-magneto-hydrodynamic |
| url | https://doi.org/10.1038/s41598-025-88877-6 |
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