Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study
The non-Newtonian fluids are used in several operations, including the transportation of crude oil, drilling fluids, and hydraulic fracturing fluids. These fluids' flow characteristics can be described by the Carreau model, which helps with the planning and improvement of manufacturing and tran...
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Elsevier B.V.
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43751/1/Neural%20networking%20analysis%20of%20thermally%20magnetized%20mass%20transfer%20coefficient.pdf http://umpir.ump.edu.my/id/eprint/43751/ https://doi.org/10.1016/j.ijft.2025.101069 |
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| author | Ur Rehman, Khalil Shatanawi, Wasfi Asghar, Zeeshan Abdul Rahman, Mohd Kasim |
| author_facet | Ur Rehman, Khalil Shatanawi, Wasfi Asghar, Zeeshan Abdul Rahman, Mohd Kasim |
| author_sort | Ur Rehman, Khalil |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
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| continent | Asia |
| country | Malaysia |
| description | The non-Newtonian fluids are used in several operations, including the transportation of crude oil, drilling fluids, and hydraulic fracturing fluids. These fluids' flow characteristics can be described by the Carreau model, which helps with the planning and improvement of manufacturing and transportation procedures. Owing to such motivation we have considered the Carreau fluid flow subject to a magnetized flat surface with porosity, heat generation, temperature slip, chemical reaction, and velocity slip effects. The problem is formulated as coupled differential equations. For solution purposes, the order of equations is reduced by performing Lie symmetry analysis. The compact equations are further solved by the shooting method. The evaluation of the mass transfer coefficient for the Carreau fluid model is done by using an Artificial Intelligence based neural model. The Schmidt number, porosity, magnetic, Weissenberg number, and chemical reaction parameters are treated as inputs while the mass transfer rate is taken as output. Owing to 10 neurons in the hidden layer, the network is trained by the Levenberg-Marquardt algorithm. It is found that the mass transfer rate exhibits a direct relation with the Schmidt number and chemical reaction parameter. The magnitude of the Carreau concentration is perceived to be higher for non-porous surfaces when the chemical reaction parameter and Schmidt number exhibit positive change. |
| format | Article |
| id | my.ump.umpir.43751 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| record_format | eprints |
| spelling | my.ump.umpir.437512025-02-06T04:42:44Z http://umpir.ump.edu.my/id/eprint/43751/ Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study Ur Rehman, Khalil Shatanawi, Wasfi Asghar, Zeeshan Abdul Rahman, Mohd Kasim QA Mathematics The non-Newtonian fluids are used in several operations, including the transportation of crude oil, drilling fluids, and hydraulic fracturing fluids. These fluids' flow characteristics can be described by the Carreau model, which helps with the planning and improvement of manufacturing and transportation procedures. Owing to such motivation we have considered the Carreau fluid flow subject to a magnetized flat surface with porosity, heat generation, temperature slip, chemical reaction, and velocity slip effects. The problem is formulated as coupled differential equations. For solution purposes, the order of equations is reduced by performing Lie symmetry analysis. The compact equations are further solved by the shooting method. The evaluation of the mass transfer coefficient for the Carreau fluid model is done by using an Artificial Intelligence based neural model. The Schmidt number, porosity, magnetic, Weissenberg number, and chemical reaction parameters are treated as inputs while the mass transfer rate is taken as output. Owing to 10 neurons in the hidden layer, the network is trained by the Levenberg-Marquardt algorithm. It is found that the mass transfer rate exhibits a direct relation with the Schmidt number and chemical reaction parameter. The magnitude of the Carreau concentration is perceived to be higher for non-porous surfaces when the chemical reaction parameter and Schmidt number exhibit positive change. Elsevier B.V. 2025-03 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/43751/1/Neural%20networking%20analysis%20of%20thermally%20magnetized%20mass%20transfer%20coefficient.pdf Ur Rehman, Khalil and Shatanawi, Wasfi and Asghar, Zeeshan and Abdul Rahman, Mohd Kasim (2025) Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study. International Journal of Thermofluids, 26 (101069). pp. 1-11. ISSN 2666-2027. (Published) https://doi.org/10.1016/j.ijft.2025.101069 https://doi.org/10.1016/j.ijft.2025.101069 |
| spellingShingle | QA Mathematics Ur Rehman, Khalil Shatanawi, Wasfi Asghar, Zeeshan Abdul Rahman, Mohd Kasim Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study |
| title | Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study |
| title_full | Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study |
| title_fullStr | Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study |
| title_full_unstemmed | Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study |
| title_short | Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study |
| title_sort | neural networking analysis of thermally magnetized mass transfer coefficient (mtc) for carreau fluid flow: a comparative study |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/43751/1/Neural%20networking%20analysis%20of%20thermally%20magnetized%20mass%20transfer%20coefficient.pdf http://umpir.ump.edu.my/id/eprint/43751/ https://doi.org/10.1016/j.ijft.2025.101069 https://doi.org/10.1016/j.ijft.2025.101069 |
| url_provider | http://umpir.ump.edu.my/ |