Abstract
Rotational dusty fluid flow refers to the motion of a mixture of fluid and solid particles in a rotating frame of reference. Various industrial processes, such as oil drilling, chemical processing, and materials manufacturing, have applications of rotational dusty fluid flow. Studying the dynamics of rotational dusty fluid flow is crucial for optimizing these industrial processes and improving their efficiency. The research focuses on understanding the behavior of a dusty fluid in a horizontal channel subjected to the combined influences of rotation and a magnetic field. The flow is driven by a constant pressure gradient and the movement of the upper plate, with the fluid flowing between two parallel plates. To analyze this system, a set of coupled partial differential equations governing the motion of the fluid and dust particles is developed. These equations account for both primary and secondary velocity components of the fluid and dust. To solve them, the study employs a meshfree radial basis function pseudospectral method. This advanced numerical technique is known for its flexibility in solving complex systems of partial differential equations without requiring structured grids, enabling high accuracy even in scenarios with irregular geometries or boundary conditions. The computed velocity profiles are then used to evaluate the pumping power needed to sustain flow in the absence of the pressure gradient. Results are presented through graphical analysis, showcasing the effects of key fluid parameters such as the Coriolis frequency parameter, dust particle concentration parameter, Reynolds number, Ekman number, ion slip parameter, and Hall parameter. Notably, the findings reveal that an increase in the Coriolis frequency reduces the primary velocity while increasing the secondary velocity. This behavior arises because the Coriolis force, which acts perpendicular to the flow direction, distorts the velocity profile, creating a complex interplay between rotational and flow dynamics.
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Research ethics: Not available.
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Informed consent: Not available.
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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: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
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