Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials
Abstract This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-58595-6 |
| _version_ | 1797199424181501952 |
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| author | Muhammad Sulaiman Osamah Ibrahim Khalaf Naveed Ahmad Khan Fahad Sameer Alshammari Sameer Algburi Habib Hamam |
| author_facet | Muhammad Sulaiman Osamah Ibrahim Khalaf Naveed Ahmad Khan Fahad Sameer Alshammari Sameer Algburi Habib Hamam |
| author_sort | Muhammad Sulaiman |
| collection | DOAJ |
| description | Abstract This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space. |
| first_indexed | 2024-04-24T07:15:32Z |
| format | Article |
| id | doaj.art-c2985364304f42c299a3761a8b8be61b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T07:15:32Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c2985364304f42c299a3761a8b8be61b2024-04-21T11:19:28ZengNature PortfolioScientific Reports2045-23222024-04-0114111410.1038/s41598-024-58595-6Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materialsMuhammad Sulaiman0Osamah Ibrahim Khalaf1Naveed Ahmad Khan2Fahad Sameer Alshammari3Sameer Algburi4Habib Hamam5Department of Mathematics, Abdul Wali Khan UniversityDepartment of Solar, Al-Nahrain Research Center for Renewable Energy, Al-Nahrain UniversitySchool of Information Technology and Systems, University of CanberraDepartment of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam bin Abdulaziz UniversityDepartment of Computer Engineering Technologies, Al-Kitab UniversityFaculty of Engineering, Uni de MonctonAbstract This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.https://doi.org/10.1038/s41598-024-58595-6Funtionally graded finHeat transferTemperature distributionMachine learningThermal analysisSupervised neural networks |
| spellingShingle | Muhammad Sulaiman Osamah Ibrahim Khalaf Naveed Ahmad Khan Fahad Sameer Alshammari Sameer Algburi Habib Hamam Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials Scientific Reports Funtionally graded fin Heat transfer Temperature distribution Machine learning Thermal analysis Supervised neural networks |
| title | Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials |
| title_full | Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials |
| title_fullStr | Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials |
| title_full_unstemmed | Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials |
| title_short | Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials |
| title_sort | machine learning based prediction of heat transfer performance in annular fins with functionally graded materials |
| topic | Funtionally graded fin Heat transfer Temperature distribution Machine learning Thermal analysis Supervised neural networks |
| url | https://doi.org/10.1038/s41598-024-58595-6 |
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