Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: Leveraging machine learning and CFD approach
Buoyancy-driven convection resulting from the combined buoyancy is commonly observed in various engineering applications, particularly for equipment located within confined enclosures. The structure of these enclosures significantly influences the convective flow and the subsequent heat and mass tra...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019929 |
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| Summary: | Buoyancy-driven convection resulting from the combined buoyancy is commonly observed in various engineering applications, particularly for equipment located within confined enclosures. The structure of these enclosures significantly influences the convective flow and the subsequent heat and mass transfer characteristics. This investigation reports a combined numerical and ANN-based analysis of buoyancy-assisted convection from thermosolutal convection due to compositional buoyancies in a tilted porous parallelogram-shaped enclosure. The enclosure's vertical sloping sidewalls contain a thermal or solute source and sink at different temperatures or concentrations, with the remaining sections of sidewalls considered adiabatic or impermeable. The governing equations, based on Darcy's law, are solved using finite difference-based time-splitting and relaxation methods. Extensive numerical calculations are conducted for a broad range of parameters, including Rayleigh numbers, the tilt angle of the parallelogrammic geometry, the tilt angle of sloping sidewalls, the Hartmann number, the Lewis number, the buoyancy ratio and source-sink configurations. The primary goal of this research is to examine and evaluate the impact of two tilt angles, α and ϕ, formed by the enclosure and its inclined sidewalls, respectively, on thermosolutal convection in porous parallelogrammic geometry. Through comprehensive and methodical numerical simulations, the effects of all parameters are thoroughly captured. The findings reveal that the geometric tilt angle significantly affects the flow structure, thermal and solute transport processes compared to a non-inclined parallelogrammic enclosure. Moreover, the enclosure's tilt angle plays a more dominant role in altering the flow pattern, heat and mass transfer performances than the tilt angle of the sloping walls. In addition, an artificial neural network (ANN) model is developed that uses a softmax activation function to predict Nu‾ and Sh‾, and its performance is evaluated against a regression model, yielding robust results. |
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| ISSN: | 2590-1230 |