Intelligent computing for magnetohydrodynamic micropolar nanofluid with stratification using Levenberg–Marquardt backpropagation algorithm
The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of prob...
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| Vydáno v: | Journal of computational science Ročník 92; s. 102727 |
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Elsevier B.V
01.12.2025
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| ISSN: | 1877-7503 |
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| Abstract | The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of problems with boundary values, which are resolved utilizing the proposed method LMBB algorithm and a numerical technique BVP4c. The LMBB approach is an iterative approach for figuring out the least of a function that is not linear, is distinct as the addition of squares. The outcomes are also cross-checked against those of earlier studies and the MATLAB’s BVP4c solver for validation. The mapping of velocity, concentration and temperature profiles from the input to results is another use of neural networking. These results show the accuracy level of the predictions and improvements made by ANN. To generalize a dataset, the BVP4c techniques’ performance is utilized to lower error of mean square. Data based on the ratio of training (80 %), validation (10 %) and testing (10 %) is used by the ANN-based LMBB backpropagation optimization technique. Histograms and function fitness are utilized to verify the algorithm’s dependability. For fluid dynamics, numerical methods and ANN perform incredibly well together, and this could result in new developments across a wide range of fields. The results of this study may aid in the optimization of fluid systems, leading to higher productivity and efficiency in a range of engineering applications. |
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| AbstractList | The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of problems with boundary values, which are resolved utilizing the proposed method LMBB algorithm and a numerical technique BVP4c. The LMBB approach is an iterative approach for figuring out the least of a function that is not linear, is distinct as the addition of squares. The outcomes are also cross-checked against those of earlier studies and the MATLAB’s BVP4c solver for validation. The mapping of velocity, concentration and temperature profiles from the input to results is another use of neural networking. These results show the accuracy level of the predictions and improvements made by ANN. To generalize a dataset, the BVP4c techniques’ performance is utilized to lower error of mean square. Data based on the ratio of training (80 %), validation (10 %) and testing (10 %) is used by the ANN-based LMBB backpropagation optimization technique. Histograms and function fitness are utilized to verify the algorithm’s dependability. For fluid dynamics, numerical methods and ANN perform incredibly well together, and this could result in new developments across a wide range of fields. The results of this study may aid in the optimization of fluid systems, leading to higher productivity and efficiency in a range of engineering applications. |
| ArticleNumber | 102727 |
| Author | Ullah, Ikram Hassan, Waqar Ul Zhang, Hong-Na Li, Feng-Chen Shukat, Saira Haq, Ikram Ul Li, Xiao-Bin |
| Author_xml | – sequence: 1 givenname: Ikram Ul surname: Haq fullname: Haq, Ikram Ul organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 2 givenname: Saira surname: Shukat fullname: Shukat, Saira organization: Department of Mathematics, University of Sialkot, Sialkot 51040, Pakistan – sequence: 3 givenname: Ikram surname: Ullah fullname: Ullah, Ikram organization: Department of Natural Sciences and Humanities, University of Engineering and Technology, Mardan 23200, Pakistan – sequence: 4 givenname: Waqar Ul surname: Hassan fullname: Hassan, Waqar Ul organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 5 givenname: Hong-Na surname: Zhang fullname: Zhang, Hong-Na organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 6 givenname: Xiao-Bin surname: Li fullname: Li, Xiao-Bin organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 7 givenname: Feng-Chen orcidid: 0000-0003-3271-7949 surname: Li fullname: Li, Feng-Chen email: lifch@tju.edu.cn organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China |
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| Cites_doi | 10.1108/HFF-02-2021-0103 10.1063/1.4935649 10.1002/adfm.201906041 10.1007/s10973-018-7482-6 10.1038/s41598-022-06458-3 10.1155/2022/9888379 10.3390/fluids7030116 10.1142/S0217979223502375 10.1080/10407782.2023.2212861 10.1007/s00162-020-00542-y 10.1016/0020-7225(64)90005-9 10.1007/s10409-021-01154-3 10.1038/s41598-021-97458-2 10.1186/1556-276X-6-300 10.1080/17458080.2017.1285445 10.1007/s10973-019-08434-y 10.3390/en15010316 10.1016/j.aej.2024.10.003 10.3390/cryst11111315 10.1016/j.csite.2024.104378 10.1016/0022-247X(72)90106-0 10.1016/j.petrol.2018.04.019 |
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| Keywords | Levenberg-Marquardt Riga plate Nanofluid Double stratification, Suction/Injection Machine learning MHD Artificial neural network |
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| SubjectTerms | Artificial neural network Double stratification, Suction/Injection Levenberg-Marquardt Machine learning MHD Nanofluid Riga plate |
| Title | Intelligent computing for magnetohydrodynamic micropolar nanofluid with stratification using Levenberg–Marquardt backpropagation algorithm |
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