Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel
The application of artificial neural network has kept the whole world amazed, as it has flourished its roots to analyse different domains of science and technology. The current article is modelled to bear witness to how the artificial neural network is administered to study heat transfer and fluid f...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Partial Differential Equations in Applied Mathematics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666818124004479 |
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| author | Pradeep Kumar Felicita Almeida Ajaykumar AR Qasem Al-Mdallal |
| author_facet | Pradeep Kumar Felicita Almeida Ajaykumar AR Qasem Al-Mdallal |
| author_sort | Pradeep Kumar |
| collection | DOAJ |
| description | The application of artificial neural network has kept the whole world amazed, as it has flourished its roots to analyse different domains of science and technology. The current article is modelled to bear witness to how the artificial neural network is administered to study heat transfer and fluid flow problems. The model constructed analyses the mixed convective and unsteady flow of micropolar fluid through the microchannel in the presence of activation energy and magnetic field using Buongiorno's model. No slip and convective boundary conditions are employed. The partial differential equation is solved using the finite difference method. The artificial neural network using the Levenberg-Marquardt algorithm with the feed-forward backpropagation method is constructed and trained. The results of the analysis show that the material parameter lowers the fluid's velocity. For higher magnetic effects, the micro-rotation profile maximises at left half and minimises at right half of microchannel. The temperature profile increases with increasing Eckert number and thermophoresis parameter. The reaction rate parameter is a depleting function, while the activation energy parameter is an enhancing function of the solutal profile. The results obtained from the artificial neural network for all 8 scenarios are highly reliable due to its high accuracy, which is pleasantly deliberated by the mean square error values, error histograms, training, and regression graphs of the neural network model. The absolute error analysis carried out is in the range of 10−4 to 10−5. The prominent conclusion from the analysis is that artificial neural network is sophisticated tool to predict the subsequent sequel of fluid flow and heat transport over a long period of time, reducing computational time to solve complicated fluid flow problems. |
| format | Article |
| id | doaj-art-714d4e419745467892acbb0d3a50607c |
| institution | Kabale University |
| issn | 2666-8181 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Partial Differential Equations in Applied Mathematics |
| spelling | doaj-art-714d4e419745467892acbb0d3a50607c2025-01-12T05:26:09ZengElsevierPartial Differential Equations in Applied Mathematics2666-81812025-03-0113101061Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannelPradeep Kumar0Felicita Almeida1Ajaykumar AR2Qasem Al-Mdallal3Department of Mathematics, School of Engineering, Presidency University, Rajanakunte, Yelahanka, Bengaluru 560064, Karnataka, IndiaDepartment of Mathematics, JNN College of Engineering, Shivamogga, Karnataka, IndiaDepartment of Mathematics, School of Engineering, Presidency University, Rajanakunte, Yelahanka, Bengaluru 560064, Karnataka, IndiaDepartment of Mathematical Sciences, P.O. Box 17551, UAE University, Al-Ain, United Arab Emirates; Corresponding author.The application of artificial neural network has kept the whole world amazed, as it has flourished its roots to analyse different domains of science and technology. The current article is modelled to bear witness to how the artificial neural network is administered to study heat transfer and fluid flow problems. The model constructed analyses the mixed convective and unsteady flow of micropolar fluid through the microchannel in the presence of activation energy and magnetic field using Buongiorno's model. No slip and convective boundary conditions are employed. The partial differential equation is solved using the finite difference method. The artificial neural network using the Levenberg-Marquardt algorithm with the feed-forward backpropagation method is constructed and trained. The results of the analysis show that the material parameter lowers the fluid's velocity. For higher magnetic effects, the micro-rotation profile maximises at left half and minimises at right half of microchannel. The temperature profile increases with increasing Eckert number and thermophoresis parameter. The reaction rate parameter is a depleting function, while the activation energy parameter is an enhancing function of the solutal profile. The results obtained from the artificial neural network for all 8 scenarios are highly reliable due to its high accuracy, which is pleasantly deliberated by the mean square error values, error histograms, training, and regression graphs of the neural network model. The absolute error analysis carried out is in the range of 10−4 to 10−5. The prominent conclusion from the analysis is that artificial neural network is sophisticated tool to predict the subsequent sequel of fluid flow and heat transport over a long period of time, reducing computational time to solve complicated fluid flow problems.http://www.sciencedirect.com/science/article/pii/S2666818124004479Artificial neural networkLevenberg Marquardt algorithmMicropolar nanofluidBuongiorno modelActivation energy |
| spellingShingle | Pradeep Kumar Felicita Almeida Ajaykumar AR Qasem Al-Mdallal Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel Partial Differential Equations in Applied Mathematics Artificial neural network Levenberg Marquardt algorithm Micropolar nanofluid Buongiorno model Activation energy |
| title | Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel |
| title_full | Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel |
| title_fullStr | Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel |
| title_full_unstemmed | Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel |
| title_short | Artificial neural network model using Levenberg Marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel |
| title_sort | artificial neural network model using levenberg marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel |
| topic | Artificial neural network Levenberg Marquardt algorithm Micropolar nanofluid Buongiorno model Activation energy |
| url | http://www.sciencedirect.com/science/article/pii/S2666818124004479 |
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