Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing
Abstract Fins and radial fins are versatile engineering components that significantly enhance heat transfer and thermal management in diverse applications, hence improving efficiency and performance across several sectors. This study examines the temperature distribution in a radial porous fin under...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82017-2 |
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| author | Shazia Habib Waseem Zeeshan Khan Salah Boulaaras Mati ur Rahman Saeed Islam Rafik Guefaifia |
| author_facet | Shazia Habib Waseem Zeeshan Khan Salah Boulaaras Mati ur Rahman Saeed Islam Rafik Guefaifia |
| author_sort | Shazia Habib |
| collection | DOAJ |
| description | Abstract Fins and radial fins are versatile engineering components that significantly enhance heat transfer and thermal management in diverse applications, hence improving efficiency and performance across several sectors. This study examines the temperature distribution in a radial porous fin under steady-state conditions, evaluating the impact of several significant parameters by utilizing a novel methodology. We specifically introduce an inclined magnetic field and examine the effects of convection and internal heat generation on the thermal behavior of the fin. We employ the Levenberg Marquard Backpropagation Neural Network Algorithm. We initially obtain the data with the bvp4c solver. This novel methodology demonstrates commendable performance, by its mean squared error and its gradient which are mentioned in their figures along with absolute error. Furthermore, increase in the parameters of heat generation and ambient temperature, results in a tendency for the temperature profile to rise. In contrast, as convection-conduction parameter, porosity parameter and Hartmann number increase, the temperature profile decreases. This innovative approach offers a sophisticated solution for complex thermal models, improved prediction accuracy for nonlinear heat transfer, parameter-driven optimization in porous media heat transfer, and increased model efficiency for real-time thermal management. |
| format | Article |
| id | doaj-art-052582d3c35542c88b77d1c9c8908657 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-052582d3c35542c88b77d1c9c89086572024-12-29T12:22:04ZengNature PortfolioScientific Reports2045-23222024-12-0114111910.1038/s41598-024-82017-2Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computingShazia Habib0Waseem1Zeeshan Khan2Salah Boulaaras3Mati ur Rahman4Saeed Islam5Rafik Guefaifia6Department of Mathematics, Abdul Wali Khan UniversitySchool of Mechanical Engineering, Jiangsu UniversityDepartment of Mathematics, Abdul Wali Khan UniversityDepartment of Mathematics, College of Science, Qassim UniversitySchool of Mathematical Sciences, Jiangsu UniversityDepartment of Mathematics, Abdul Wali Khan UniversityDepartment of Mathematics, College of Science, Qassim UniversityAbstract Fins and radial fins are versatile engineering components that significantly enhance heat transfer and thermal management in diverse applications, hence improving efficiency and performance across several sectors. This study examines the temperature distribution in a radial porous fin under steady-state conditions, evaluating the impact of several significant parameters by utilizing a novel methodology. We specifically introduce an inclined magnetic field and examine the effects of convection and internal heat generation on the thermal behavior of the fin. We employ the Levenberg Marquard Backpropagation Neural Network Algorithm. We initially obtain the data with the bvp4c solver. This novel methodology demonstrates commendable performance, by its mean squared error and its gradient which are mentioned in their figures along with absolute error. Furthermore, increase in the parameters of heat generation and ambient temperature, results in a tendency for the temperature profile to rise. In contrast, as convection-conduction parameter, porosity parameter and Hartmann number increase, the temperature profile decreases. This innovative approach offers a sophisticated solution for complex thermal models, improved prediction accuracy for nonlinear heat transfer, parameter-driven optimization in porous media heat transfer, and increased model efficiency for real-time thermal management.https://doi.org/10.1038/s41598-024-82017-2Porous radial fin modelInclined magnetic fieldLevenberg Marquard Backpropagation neural network algorithmArtificial neural networks, Nonlinear equations |
| spellingShingle | Shazia Habib Waseem Zeeshan Khan Salah Boulaaras Mati ur Rahman Saeed Islam Rafik Guefaifia Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing Scientific Reports Porous radial fin model Inclined magnetic field Levenberg Marquard Backpropagation neural network algorithm Artificial neural networks, Nonlinear equations |
| title | Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing |
| title_full | Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing |
| title_fullStr | Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing |
| title_full_unstemmed | Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing |
| title_short | Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing |
| title_sort | analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing |
| topic | Porous radial fin model Inclined magnetic field Levenberg Marquard Backpropagation neural network algorithm Artificial neural networks, Nonlinear equations |
| url | https://doi.org/10.1038/s41598-024-82017-2 |
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