Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach
The objective of this study is to examine heat and mass transfer aspects of ternary nanofluid flow in coaxial cylinder under the influence of Arrhenius activation energy, microorganisms’ concentration and bioconvection Peclet number, which a pivotal rolet in various scientific and engineering applic...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Thermofluids |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666202725002411 |
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| author | s Manjunatha Khalil Ur Rehman Wasfi Shatanawi Tanuja T N |
| author_facet | s Manjunatha Khalil Ur Rehman Wasfi Shatanawi Tanuja T N |
| author_sort | s Manjunatha |
| collection | DOAJ |
| description | The objective of this study is to examine heat and mass transfer aspects of ternary nanofluid flow in coaxial cylinder under the influence of Arrhenius activation energy, microorganisms’ concentration and bioconvection Peclet number, which a pivotal rolet in various scientific and engineering applications. The flow of ternary nanofluid is caused due to stretching inner cylinder with stationary outer cylinder. The nonlinear partial equations are derived for the flow model and reduced to non-linear ordinary differential equation by applying suitable similarity transformation. The resultant equations are resolved mathematically using Runge Kutta Fehlberg (RKF45) technique. The obtained numerical results are validated with the published work to check the exactness of the solution methodology and it is noticed that the present outcomes are on par with published work. The physical behaviour of the pertinent parameters is analysed through graphical depiction. The derived quantities like drag force and Sherwood number are studied through tabular column. Additionally, the heat transfer rate is analysed by using backpropagated Levenberg-Marquardt Machine learning algorithm. Further, the correlation between the parameter on the rate of heat transfer is analysed by using Mean square error and regression graphs. The key outcome of this research is that, the temperature upsurges by increasing the solid volume of nanoparticle due to higher thermal conductivity of the nanoparticles. Further, it is perceived from the artificial neural network model that, the correlation between the input parameters and output data are strongly correlated (R = 1). |
| format | Article |
| id | doaj-art-99aece70def64bb49eb1eae87338429b |
| institution | Kabale University |
| issn | 2666-2027 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Thermofluids |
| spelling | doaj-art-99aece70def64bb49eb1eae87338429b2025-08-20T03:31:23ZengElsevierInternational Journal of Thermofluids2666-20272025-07-012810129410.1016/j.ijft.2025.101294Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approachs Manjunatha0Khalil Ur Rehman1Wasfi Shatanawi2Tanuja T N3Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560076, Karnataka, IndiaDepartment of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia; Corresponding author.Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia; Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa, 13133, JordanDepartment of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560076, Karnataka, IndiaThe objective of this study is to examine heat and mass transfer aspects of ternary nanofluid flow in coaxial cylinder under the influence of Arrhenius activation energy, microorganisms’ concentration and bioconvection Peclet number, which a pivotal rolet in various scientific and engineering applications. The flow of ternary nanofluid is caused due to stretching inner cylinder with stationary outer cylinder. The nonlinear partial equations are derived for the flow model and reduced to non-linear ordinary differential equation by applying suitable similarity transformation. The resultant equations are resolved mathematically using Runge Kutta Fehlberg (RKF45) technique. The obtained numerical results are validated with the published work to check the exactness of the solution methodology and it is noticed that the present outcomes are on par with published work. The physical behaviour of the pertinent parameters is analysed through graphical depiction. The derived quantities like drag force and Sherwood number are studied through tabular column. Additionally, the heat transfer rate is analysed by using backpropagated Levenberg-Marquardt Machine learning algorithm. Further, the correlation between the parameter on the rate of heat transfer is analysed by using Mean square error and regression graphs. The key outcome of this research is that, the temperature upsurges by increasing the solid volume of nanoparticle due to higher thermal conductivity of the nanoparticles. Further, it is perceived from the artificial neural network model that, the correlation between the input parameters and output data are strongly correlated (R = 1).http://www.sciencedirect.com/science/article/pii/S2666202725002411Heat transferActivation energyTernary nanofluidCoaxial cylinderNeural network |
| spellingShingle | s Manjunatha Khalil Ur Rehman Wasfi Shatanawi Tanuja T N Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach International Journal of Thermofluids Heat transfer Activation energy Ternary nanofluid Coaxial cylinder Neural network |
| title | Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach |
| title_full | Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach |
| title_fullStr | Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach |
| title_full_unstemmed | Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach |
| title_short | Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach |
| title_sort | statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder artificial neural network approach |
| topic | Heat transfer Activation energy Ternary nanofluid Coaxial cylinder Neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2666202725002411 |
| work_keys_str_mv | AT smanjunatha statisticalthermalstudyofternaryhybridnanofluidflowincoaxialcylinderartificialneuralnetworkapproach AT khalilurrehman statisticalthermalstudyofternaryhybridnanofluidflowincoaxialcylinderartificialneuralnetworkapproach AT wasfishatanawi statisticalthermalstudyofternaryhybridnanofluidflowincoaxialcylinderartificialneuralnetworkapproach AT tanujatn statisticalthermalstudyofternaryhybridnanofluidflowincoaxialcylinderartificialneuralnetworkapproach |