Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity
One of the main reasons for the popularity of ANNs is their wonderful capability to handle very complex and nonlinear mathematical problems. It can, therefore, provide a very valuable computational framework wide in applications, from biotechnology to biological computation and fluid dynamics. In th...
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Elsevier
2025-02-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X24017337 |
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| author | Umar Farooq Shan Ali Khan Haihu Liu Muhammad Imran Lotfi Ben Said Aleena Ramzan Taseer Muhammad |
| author_facet | Umar Farooq Shan Ali Khan Haihu Liu Muhammad Imran Lotfi Ben Said Aleena Ramzan Taseer Muhammad |
| author_sort | Umar Farooq |
| collection | DOAJ |
| description | One of the main reasons for the popularity of ANNs is their wonderful capability to handle very complex and nonlinear mathematical problems. It can, therefore, provide a very valuable computational framework wide in applications, from biotechnology to biological computation and fluid dynamics. In this article, computational ANN paradigms are utilized in the analysis of boundary layer flow and heat transfer of magnetized Casson fluid over a nonlinearly stretching elastic sheet under velocity slip conditions. Casson fluid behavior under the influence of magnetohydrodynamics with the influence of heat generation, temperature-dependent dynamic viscosity, variable thermal conductivity, and viscous dissipation are considered in this study. With the help of a similarity transformation, the complex partial differential equations governing the flow and energy take on the form of nonlinear ordinary differential equations. These resulting nonlinear ordinary differential equations are solved by using the bvp4c solver in MATLAB. The numerical solutions generated from the bvp4c solver for the problem under consideration are used to develop the reference dataset for the anticipated radiative Casson fluid Levenberg-Marquardt backpropagation neural network (LMT-ABPNN). Finally, developed dataset features were applied to the artificial intelligence-based LMT-ABPNN procedure to validate the numerical results for radiative Casson fluid. This LMT-ABPNN is trained, tested, and validated in predicting the approximate numerical results for RCF under various conditions. The proposed LMT-ABPNN performance validation is carried out based on mean squared error fitness, error histograms, and regression analysis. Results obtained for regression metrics, absolute error, MSE and error histogram plots through the LMT-ABPNN architecture do confirm the superior performance attained here. From the results, the study raises close concordance with respect to reference data. The low MSE indicates that the model is fit to predict with good accuracy, but the minimal absolute error close to zero proves the prediction accuracy of the developed approach at its best. |
| format | Article |
| id | doaj-art-36e0a52794a948098b30678d5b32ace0 |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-36e0a52794a948098b30678d5b32ace02025-02-02T05:27:12ZengElsevierCase Studies in Thermal Engineering2214-157X2025-02-0166105702Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivityUmar Farooq0Shan Ali Khan1Haihu Liu2Muhammad Imran3Lotfi Ben Said4Aleena Ramzan5Taseer Muhammad6School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaSchool of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; Department of Mathematics, Government College University Faisalabad, 38000, PakistanSchool of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; Corresponding author.Department of Mathematics, Government College University Faisalabad, 38000, Pakistan; Biruni University, Education Faculty, Department of Mathematics and Statistics Education, Istanbul, TurkeyDepartment of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi Arabia; Laboratory of Electrochemistry and Environment (LEE), National Engineering School of Sfax, University of Sfax, Sfax, 5080, TunisiaDepartment of Zoology, Division of Science and Technology, University of Education, Lahore, PakistanDepartment of Mathematics, College of Sciences, King Khalid University, Abha, 61413, Saudi ArabiaOne of the main reasons for the popularity of ANNs is their wonderful capability to handle very complex and nonlinear mathematical problems. It can, therefore, provide a very valuable computational framework wide in applications, from biotechnology to biological computation and fluid dynamics. In this article, computational ANN paradigms are utilized in the analysis of boundary layer flow and heat transfer of magnetized Casson fluid over a nonlinearly stretching elastic sheet under velocity slip conditions. Casson fluid behavior under the influence of magnetohydrodynamics with the influence of heat generation, temperature-dependent dynamic viscosity, variable thermal conductivity, and viscous dissipation are considered in this study. With the help of a similarity transformation, the complex partial differential equations governing the flow and energy take on the form of nonlinear ordinary differential equations. These resulting nonlinear ordinary differential equations are solved by using the bvp4c solver in MATLAB. The numerical solutions generated from the bvp4c solver for the problem under consideration are used to develop the reference dataset for the anticipated radiative Casson fluid Levenberg-Marquardt backpropagation neural network (LMT-ABPNN). Finally, developed dataset features were applied to the artificial intelligence-based LMT-ABPNN procedure to validate the numerical results for radiative Casson fluid. This LMT-ABPNN is trained, tested, and validated in predicting the approximate numerical results for RCF under various conditions. The proposed LMT-ABPNN performance validation is carried out based on mean squared error fitness, error histograms, and regression analysis. Results obtained for regression metrics, absolute error, MSE and error histogram plots through the LMT-ABPNN architecture do confirm the superior performance attained here. From the results, the study raises close concordance with respect to reference data. The low MSE indicates that the model is fit to predict with good accuracy, but the minimal absolute error close to zero proves the prediction accuracy of the developed approach at its best.http://www.sciencedirect.com/science/article/pii/S2214157X24017337Artificial neural networkCasson fluid modelMagnetohydrodynamicsHeat generationTemperature-dependent dynamic viscosityVariable thermal conductivity |
| spellingShingle | Umar Farooq Shan Ali Khan Haihu Liu Muhammad Imran Lotfi Ben Said Aleena Ramzan Taseer Muhammad Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity Case Studies in Thermal Engineering Artificial neural network Casson fluid model Magnetohydrodynamics Heat generation Temperature-dependent dynamic viscosity Variable thermal conductivity |
| title | Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity |
| title_full | Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity |
| title_fullStr | Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity |
| title_full_unstemmed | Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity |
| title_short | Application of artificial intelligence brain structure-based paradigm to predict the slip condition impact on magnetized thermal Casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity |
| title_sort | application of artificial intelligence brain structure based paradigm to predict the slip condition impact on magnetized thermal casson viscoplastic fluid model under combined temperature dependent viscosity and thermal conductivity |
| topic | Artificial neural network Casson fluid model Magnetohydrodynamics Heat generation Temperature-dependent dynamic viscosity Variable thermal conductivity |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X24017337 |
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