Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink
This study focuses on the application of ternary hybrid nanofluids (manganese zinc ferrite, copper, and silver) over a spinning disk, which has significant implications for thermal management, biomedical devices, aerospace, and industrial cooling systems. Due to the antibacterial and antifungicidal...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-06-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251341968 |
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| author | Ibrahim Mahariq Kashif Ullah Mehreen Fiza Aasim Ullah Jan Hakeem Ullah Saeed Islam Seham M. Al-Mekhlafi |
| author_facet | Ibrahim Mahariq Kashif Ullah Mehreen Fiza Aasim Ullah Jan Hakeem Ullah Saeed Islam Seham M. Al-Mekhlafi |
| author_sort | Ibrahim Mahariq |
| collection | DOAJ |
| description | This study focuses on the application of ternary hybrid nanofluids (manganese zinc ferrite, copper, and silver) over a spinning disk, which has significant implications for thermal management, biomedical devices, aerospace, and industrial cooling systems. Due to the antibacterial and antifungicidal properties of silver ( Ag ) nanoparticles, this research also has potential applications in the food industry for sterilization and preservation. Motivated by these developments, this study investigates the Steady two-dimensional Ternary Hybrid Nanofluid Flow (STDTHNFF) problem, incorporating a nonlinear heat source-sink and Fourier heat flux model (HSFHFM) over a spinning disk. A key novelty of this work is the inclusion of a new heat source term, enhancing the thermal analysis by capturing additional energy variations. The study extensively analyzes the effects of heat sources, thermal radiation, the thermal relaxation parameter, and magnetohydrodynamic (MHD) effects, providing a more comprehensive understanding of fluid flow and heat transfer mechanisms in rotating systems. The governing coupled nonlinear partial differential equations (NLPDEs) are transformed into a dimensionless form using relevant similarity transformations. The Recurrent Neural Network-Levenberg-Marquardt Method (RNN-LMM) is employed for backpropagation, providing an efficient and accurate computational approach for solving the problem. A numerical stochastic approach is applied to evaluate training (TR), mean square errors (MSE), performance (PF), and data fitting (FT). Validation is conducted using error histograms (EH) and regression (RG) tests, ensuring high accuracy ranging from E-2 to E-7. The results demonstrate that the RNN-LMM approach effectively predicts flow characteristics with high accuracy. Graphs and numerical data reveal the influence of heat sources, thermal radiation, MHD effects, and thermal relaxation on flow behavior. The findings confirm that ternary hybrid nanofluids (THNF) enhance heat transfer rates, making them promising for industrial and engineering applications. The study highlights that heat sources significantly impact temperature distribution and heat transfer. The results of the RNN-LMM approach were compared with previous literature and found to closely align with published studies. Furthermore, these findings play a crucial role in improving thermal management systems and processes for advanced engineering and industrial applications. |
| format | Article |
| id | doaj-art-80714a0ddbb2471da8f19051feeef15b |
| institution | OA Journals |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-80714a0ddbb2471da8f19051feeef15b2025-08-20T02:35:11ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-06-011710.1177/16878132251341968Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sinkIbrahim Mahariq0Kashif Ullah1Mehreen Fiza2Aasim Ullah Jan3Hakeem Ullah4Saeed Islam5Seham M. Al-Mekhlafi6Applied Science Research Center, Applied Science Private University, Amman, JordanDepartment of Mathematics, Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, PakistanDepartment of Mathematics, Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, PakistanDepartment of Mathematics and Statistics, Bacha Khan University, Charsadda, Khyber Pakhtunkhwa,PakistanDepartment of Mathematics, Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, PakistanDepartment of Mechanical Engineering, Prince Mohammad Bin Fahd University, Al-Khobar Saudi ArabiaDepartment of Mathematics, Sana’a University, YemenThis study focuses on the application of ternary hybrid nanofluids (manganese zinc ferrite, copper, and silver) over a spinning disk, which has significant implications for thermal management, biomedical devices, aerospace, and industrial cooling systems. Due to the antibacterial and antifungicidal properties of silver ( Ag ) nanoparticles, this research also has potential applications in the food industry for sterilization and preservation. Motivated by these developments, this study investigates the Steady two-dimensional Ternary Hybrid Nanofluid Flow (STDTHNFF) problem, incorporating a nonlinear heat source-sink and Fourier heat flux model (HSFHFM) over a spinning disk. A key novelty of this work is the inclusion of a new heat source term, enhancing the thermal analysis by capturing additional energy variations. The study extensively analyzes the effects of heat sources, thermal radiation, the thermal relaxation parameter, and magnetohydrodynamic (MHD) effects, providing a more comprehensive understanding of fluid flow and heat transfer mechanisms in rotating systems. The governing coupled nonlinear partial differential equations (NLPDEs) are transformed into a dimensionless form using relevant similarity transformations. The Recurrent Neural Network-Levenberg-Marquardt Method (RNN-LMM) is employed for backpropagation, providing an efficient and accurate computational approach for solving the problem. A numerical stochastic approach is applied to evaluate training (TR), mean square errors (MSE), performance (PF), and data fitting (FT). Validation is conducted using error histograms (EH) and regression (RG) tests, ensuring high accuracy ranging from E-2 to E-7. The results demonstrate that the RNN-LMM approach effectively predicts flow characteristics with high accuracy. Graphs and numerical data reveal the influence of heat sources, thermal radiation, MHD effects, and thermal relaxation on flow behavior. The findings confirm that ternary hybrid nanofluids (THNF) enhance heat transfer rates, making them promising for industrial and engineering applications. The study highlights that heat sources significantly impact temperature distribution and heat transfer. The results of the RNN-LMM approach were compared with previous literature and found to closely align with published studies. Furthermore, these findings play a crucial role in improving thermal management systems and processes for advanced engineering and industrial applications.https://doi.org/10.1177/16878132251341968 |
| spellingShingle | Ibrahim Mahariq Kashif Ullah Mehreen Fiza Aasim Ullah Jan Hakeem Ullah Saeed Islam Seham M. Al-Mekhlafi Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink Advances in Mechanical Engineering |
| title | Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink |
| title_full | Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink |
| title_fullStr | Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink |
| title_full_unstemmed | Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink |
| title_short | Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink |
| title_sort | levenberg marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source sink |
| url | https://doi.org/10.1177/16878132251341968 |
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