Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system
The growing attractiveness of Artificial Neural Networks (ANNs) derives from their exceptional effectiveness in handling difficult and exceptionally nonlinear mathematical ideas. In complicated disciplines such as fluid mechanics, biological computation, and the field of biotechnology ANNs provide a...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Arabian Journal of Chemistry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878535223009887 |
| _version_ | 1797356133644500992 |
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| author | Muhammad Shoaib Sana Ullah Saqib Kottakkaran Sooppy Nisar Muhammad Asif Zahoor Raja Imtiaz Ali Mohammed |
| author_facet | Muhammad Shoaib Sana Ullah Saqib Kottakkaran Sooppy Nisar Muhammad Asif Zahoor Raja Imtiaz Ali Mohammed |
| author_sort | Muhammad Shoaib |
| collection | DOAJ |
| description | The growing attractiveness of Artificial Neural Networks (ANNs) derives from their exceptional effectiveness in handling difficult and exceptionally nonlinear mathematical ideas. In complicated disciplines such as fluid mechanics, biological computation, and the field of biotechnology ANNs provide a diverse computing framework that is extremely valuable. This article's major aim is to harness the capabilities of the Levenberg-Marquardt technique with backpropagation intelligent neural networks (LM- BPINNs) to study there is still a lack of clarity regarding the mechanics underlying the increased heat transfer caused by dispersed nanoparticles. The using proposed LM-BPINNs to improve the heat transmission use activation energy and Hall current phenomena in nanofluid (HTAHCNF). The data set is obtained by using Lobatto-III. A method and then ANNs is applied. The LM- BPINNs technique is applied by utilizing reference datasets, with 80% of the dataset devoted to training, 10% to testing, and 10% to verification. The precision/accuracy and converging of developed LM- BPINNs are validated based on the obtained reliability via efficient fitness achieved on mean squared error (MSE), comprehensive regression analysis, and appropriate error histogram visualizations. A diminished MSE indicates that the model's predictions are more reliable. The outcome is consistent with getting a minimal absolute error close to zero, exhibiting the effectiveness of the proposed approach. |
| first_indexed | 2024-03-08T14:21:11Z |
| format | Article |
| id | doaj.art-80df6886cafa46eea09aa0aef5a36fa4 |
| institution | Directory Open Access Journal |
| issn | 1878-5352 |
| language | English |
| last_indexed | 2024-03-08T14:21:11Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Arabian Journal of Chemistry |
| spelling | doaj.art-80df6886cafa46eea09aa0aef5a36fa42024-01-14T05:37:49ZengElsevierArabian Journal of Chemistry1878-53522024-02-01172105526Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic systemMuhammad Shoaib0Sana Ullah Saqib1Kottakkaran Sooppy Nisar2Muhammad Asif Zahoor Raja3Imtiaz Ali Mohammed4Yuan Ze University, AI Center, Taoyuan 320, TaiwanDepartment of Applied Mathematics and Statistics, Institute of Space Technology, Islamabad, PakistanDepartment of Mathematics, College of science and Humanities in Alkharj, Prince Sattam bin Abdulaziz University, 11942, Saudi Arabia; Corresponding authors.Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section.3, Douliou, Yunlin 64002, Taiwan; Corresponding authors.Department of Chemistry, College of science and Humanities in Alkharj, Prince Sattam bin Abdulaziz University, 11942, Saudi ArabiaThe growing attractiveness of Artificial Neural Networks (ANNs) derives from their exceptional effectiveness in handling difficult and exceptionally nonlinear mathematical ideas. In complicated disciplines such as fluid mechanics, biological computation, and the field of biotechnology ANNs provide a diverse computing framework that is extremely valuable. This article's major aim is to harness the capabilities of the Levenberg-Marquardt technique with backpropagation intelligent neural networks (LM- BPINNs) to study there is still a lack of clarity regarding the mechanics underlying the increased heat transfer caused by dispersed nanoparticles. The using proposed LM-BPINNs to improve the heat transmission use activation energy and Hall current phenomena in nanofluid (HTAHCNF). The data set is obtained by using Lobatto-III. A method and then ANNs is applied. The LM- BPINNs technique is applied by utilizing reference datasets, with 80% of the dataset devoted to training, 10% to testing, and 10% to verification. The precision/accuracy and converging of developed LM- BPINNs are validated based on the obtained reliability via efficient fitness achieved on mean squared error (MSE), comprehensive regression analysis, and appropriate error histogram visualizations. A diminished MSE indicates that the model's predictions are more reliable. The outcome is consistent with getting a minimal absolute error close to zero, exhibiting the effectiveness of the proposed approach.http://www.sciencedirect.com/science/article/pii/S1878535223009887NanofluidHall currentIntelligent computingNanoliquidNanoparticle aggregationBinary chemical reaction |
| spellingShingle | Muhammad Shoaib Sana Ullah Saqib Kottakkaran Sooppy Nisar Muhammad Asif Zahoor Raja Imtiaz Ali Mohammed Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system Arabian Journal of Chemistry Nanofluid Hall current Intelligent computing Nanoliquid Nanoparticle aggregation Binary chemical reaction |
| title | Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system |
| title_full | Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system |
| title_fullStr | Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system |
| title_full_unstemmed | Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system |
| title_short | Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system |
| title_sort | numerical treatment for the desirability of hall current and activation energy in the enhancement of heat transfer in a nanofluidic system |
| topic | Nanofluid Hall current Intelligent computing Nanoliquid Nanoparticle aggregation Binary chemical reaction |
| url | http://www.sciencedirect.com/science/article/pii/S1878535223009887 |
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