Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processes
Nanofluids enhance the thermal conductivity and heat transmission rate of the base fluid, reducing the risk of overhating which increase the lifespan of the coating wires. In the realm of artificial neural networks, the Levenberg-Marquardt Algorithm is characterized by its innovative stability and p...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024021212 |
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| author | Maria Altaib Badawi Zeeshan Nehad Ali Shah Imed Boukhris Adel Thaljaoui |
| author_facet | Maria Altaib Badawi Zeeshan Nehad Ali Shah Imed Boukhris Adel Thaljaoui |
| author_sort | Maria Altaib Badawi |
| collection | DOAJ |
| description | Nanofluids enhance the thermal conductivity and heat transmission rate of the base fluid, reducing the risk of overhating which increase the lifespan of the coating wires. In the realm of artificial neural networks, the Levenberg-Marquardt Algorithm is characterized by its innovative stability and produces a computational resolution of the wire covering for third grade nanofluid flow (WC-TGNFF) utilizing regression plots (RP), histogram visualizations, state transition metrics, and mean squared errors (MSE). This manuscript examines WC-TGNFF through the implementation of a novel intelligent computing system via the Levenberg-Marquardt Algorithm (ANN-LMA). The basic flow equations in expressions of PDEs for WCS-TGNFF is transformed into non-dimensional ODEs. The data acquisition for the proposed ANN-LMA is generated for parameters involved in the model WCS-TGNFF through Runge-Kutta method. The training, validation, and testing phases of ANN-LMA are employed to assess the results derived from WC-TGNFF across diverse scenarios, and a comparative analysis of the derived results is conducted against a reference dataset to evaluate the precision and efficacy of the proposed ANN-LMA framework in addressing non-Newtonian fluid challenges associated with WC-TGNFF. The novel contribution of the present model is to investigate the Brownian and thermophesis effects via ANN-LMA on the wire coating. The remarkable agreement of the proposed findings with reference solutions underscores the robustness of the framework, achieving a precision level 10−6. |
| format | Article |
| id | doaj-art-aac9068b18374b2da8e0c38cc23fe827 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-aac9068b18374b2da8e0c38cc23fe8272025-01-14T04:12:35ZengElsevierResults in Engineering2590-12302025-03-0125103878Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processesMaria Altaib Badawi0 Zeeshan1Nehad Ali Shah2Imed Boukhris3Adel Thaljaoui4Department of Computer Science and Information College of Science at Zulfi, Majmaah University, P. O. Box 66, Al-Majmaah 11952, Saudi ArabiaDepartment of Mathematics and Statistics, Bacha Khan University Charsadda, KP, Pakistan; Corresponding author.Department of Mathematics, Saveetha School of Engineering, SIMATS, Chennai 602105, Tamilnadu, IndiaDepartment of Physics, Faculty of Science, King Khalid University, P.O. Box 960, Abha, Saudi ArabiaDepartment of Computer Science and Information College of Science at Zulfi, Majmaah University, P. O. Box 66, Al-Majmaah 11952, Saudi Arabia; Preparatory Institute for Engineering Studies of Gafsa, TunisiaNanofluids enhance the thermal conductivity and heat transmission rate of the base fluid, reducing the risk of overhating which increase the lifespan of the coating wires. In the realm of artificial neural networks, the Levenberg-Marquardt Algorithm is characterized by its innovative stability and produces a computational resolution of the wire covering for third grade nanofluid flow (WC-TGNFF) utilizing regression plots (RP), histogram visualizations, state transition metrics, and mean squared errors (MSE). This manuscript examines WC-TGNFF through the implementation of a novel intelligent computing system via the Levenberg-Marquardt Algorithm (ANN-LMA). The basic flow equations in expressions of PDEs for WCS-TGNFF is transformed into non-dimensional ODEs. The data acquisition for the proposed ANN-LMA is generated for parameters involved in the model WCS-TGNFF through Runge-Kutta method. The training, validation, and testing phases of ANN-LMA are employed to assess the results derived from WC-TGNFF across diverse scenarios, and a comparative analysis of the derived results is conducted against a reference dataset to evaluate the precision and efficacy of the proposed ANN-LMA framework in addressing non-Newtonian fluid challenges associated with WC-TGNFF. The novel contribution of the present model is to investigate the Brownian and thermophesis effects via ANN-LMA on the wire coating. The remarkable agreement of the proposed findings with reference solutions underscores the robustness of the framework, achieving a precision level 10−6.http://www.sciencedirect.com/science/article/pii/S2590123024021212Wire coatingThird grade nanofluid modelArtificial neural networkLevenberg-marquardt algorithmVariable viscosity |
| spellingShingle | Maria Altaib Badawi Zeeshan Nehad Ali Shah Imed Boukhris Adel Thaljaoui Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processes Results in Engineering Wire coating Third grade nanofluid model Artificial neural network Levenberg-marquardt algorithm Variable viscosity |
| title | Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processes |
| title_full | Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processes |
| title_fullStr | Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processes |
| title_full_unstemmed | Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processes |
| title_short | Artificial neural networks analysis for non-newtonian nanofluid flow with variable viscosity and MHD effects in wire covering processes |
| title_sort | artificial neural networks analysis for non newtonian nanofluid flow with variable viscosity and mhd effects in wire covering processes |
| topic | Wire coating Third grade nanofluid model Artificial neural network Levenberg-marquardt algorithm Variable viscosity |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024021212 |
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