Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
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Vishal Chhabra
,
Chandra Shekhar Nishad
and
Manoj Sahni
Abstract
In this paper, we investigate the impact of an inclined magnetic field of uniform intensity on viscous, incompressible pressure-driven Stokes flow through a slip-patterned, rectangular microchannel using the boundary element method based on the stream function-vorticity variables approach. The present investigation focuses only on the out-phase slip patterning of the microchannel walls. We address two scenarios of slip patterning, specifically large and fine slip patterning, which are determined by the periodicity of the patterning. We utilized the no-slip and Navier’s slip boundary conditions in an alternative manner on the walls. The Stokes equations govern the viscous flow through a microchannel. We assume a very small magnetic Reynold’s number to eliminate the equation of induced magnetic field in the present study. We analyzed the impact of considered dimensionless hydrodynamic parameters, including the Hartman number (Ha), inclination angle (θ) of the magnetic field, and the slip length (ls ) on fluid dynamics. In the case of fine slip, we observed significant variations in both velocity and pressure gradient, in contrast to large slip patterning. Fine slip patterning significantly increases the shear stress at slip regimes, while large slip periodicity significantly reduces it at no-slip regimes. The present investigation has several notable implications, such as regulation and advancement of mixing and heat transmission within microfluidic systems.
Funding source: Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
Award Identifier / Grant number: Fellowship
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Research ethics: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work the authors used QuillBot and ChatGPT in order to improve readability and language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
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Conflict of interest: The authors have no conflict of interest.
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Research funding: The author Vishal Chhabra is thankful for the financial support from Pandit Deendayal Energy University, Gandhinagar, Gujarat, India, in the form of fellowship offered by the institute.
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Research Articles
- Energy cost prediction for chromium removal by nanofiltration membrane
- Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
- Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
- Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
- Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
- Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
- Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
- Ant lion based optimization for performance improvement of methanol production
- Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
- Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
