Machine learning investigation of marangoni convection in hybrid nanofluids with Darcy-Forchheimer
This research utilizes machine learning to investigate Marangoni convection in a hybrid nanofluid (MnZnFe2O4 +NiZnFe2 O4/H2 O) within a Darcy-Forchheimer porous framework. We conduct both qualitative and quantitative assessments of heat transfer, mass transfer, and viscous dissipation irreversibilit...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/456077 |
| Acceso en línea: | https://hdl.handle.net/2117/456077 https://dx.doi.org/10.1038/s41598-025-23362-8 |
| Access Level: | acceso abierto |
| Palabra clave: | Nanoscience and technology Artificial intelligence Machine Learning Levenberg Marquardt neural-network algorithm Hybrid nanofluid Darcy Forchheimer Marangoni ratio Convection Àrees temàtiques de la UPC::Física |
| Sumario: | This research utilizes machine learning to investigate Marangoni convection in a hybrid nanofluid (MnZnFe2O4 +NiZnFe2 O4/H2 O) within a Darcy-Forchheimer porous framework. We conduct both qualitative and quantitative assessments of heat transfer, mass transfer, and viscous dissipation irreversibility during the flow. Numerical results are obtained using a Python finite difference algorithm, after which MATLAB is employed for AI-based analysis. Additionally, the Levenberg-Marquardt neural network algorithm is trained and utilized. Our findings show that fluid velocity diminishes as the inverse Darcy parameter, Marangoni ratio, and Forchheimer parameter increase. Moreover, the temperature rises with the Eckert number and Prandtl ratio. As concentration increases, activation energy and Schmidt parameter also grow. Mean Square Error (MSE) for the results reaches up to 10-11 across various impacts. |
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