Hybrid nanofluid flow around a circular cylinder: A coupled ANN and numerical study of MHD, bioconvection, and thermal radiation
This study aims to examine the heat transfer properties of cross-hybrid nanofluid flow around a circular cylinder while accounting for the effects of thermal radiation, tilted magnetohydrodynamics (MHD), activation energy, and bioconvection. Improving heat transfer efficiency is the main goal of thi...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025015051 |
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| Summary: | This study aims to examine the heat transfer properties of cross-hybrid nanofluid flow around a circular cylinder while accounting for the effects of thermal radiation, tilted magnetohydrodynamics (MHD), activation energy, and bioconvection. Improving heat transfer efficiency is the main goal of this investigation. Water is used as the base fluid, and single-wall and multi-wall carbon nanotubes are combined to create a hybrid fluid. These nanoparticles' special qualities, in particular their remarkable heat conductivity is essential for several modern uses, such as electrical components, materials research, cancer therapy, refrigeration systems, and nanotechnology. The governing equations of the problem are written as partial differential equations. A boundary value problem (BVP) can be converted into an initial value problem (IVP) via the shooting method, which can then be resolved using conventional bvp4c. The algorithm bvp4c is used to assess the simplified mathematical model computationally in the MATLAB software program. The results, such as velocity, temperature, concentration, and bioconvection, are obtained graphically. The velocity profile of the hybrid nanofluid decreases as the Darcy-Forchheimer value increases. Additionally, when the thermal radiation parameter rises, so do the concentrations and temperature of hybrid nanofluids. This paper presented a new method that makes use of artificial neural networks (ANNs). To guarantee precise testing, validation, and training of the ANN model, a reliable dataset is meticulously collected and processed. The findings indicate that the (ANN) methodology is capable of accurately forecasting these values. This study provides significant information for researchers and engineers seeking to understand the characteristics of flow, its dynamics, and its predictive modeling. |
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| ISSN: | 2590-1230 |