Efficacy of transverse magnetic field towards suppressing nanofluidic flow instabilities over bluff objects
Source abstract
Abstract
The work explores the combined effect of a transverse magnetic field and nanoparticle volume fraction on the Cu-H 2 O nanofluid flow and heat transfer over circular and square bodies in an unconfined domain. The magnetic field is known to have a damping effect resulting in a stable flow. Whereas, the nanofluid volume fraction has an opposing effect, i.e., it brings instability to the flow. The Reynolds number is kept low (10 ≤ Re ≤ 30) such that the base flow goes on steady and separated around the objects with the exclusion of the magnetic field. With an addition of the nanoparticles, the flow induces instability and when such flow is subjected to the magnetic field it stabilizes and turns out to be an attached flow. This flow transition occurs at a critical magnetic field strength (1st critical Hartmann number, H a cr 1 ). With increasing nanoparticle concentration, the flow eventually turns into unsteady periodic and the magnetic field under such circumstances suppresses the vortex shedding thereby transforming the flow into a steady separated one. Hence, another critical magnetic parameter (2nd critical Hartmann number, H a cr 2 ) can be identified causing the suppression of the vortex shedding. A finite volume based numerical strategy is adopted to establish the above facts for the nanoparticle volume fraction range 1 % ≤ φ ≤ 10 %. The critical magnetic field strength is found to increase with Reynolds number and the solid fraction. However, H a cr 2 is found less compared to H a cr 1 . The variation in the Nusselt number ( Nu) with magnetic field strength is quite fascinating. H a cr 2 is found as a point of inversion of Nu variation.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanofluid Flow and Heat Transfer · Fluid Dynamics and Turbulent Flows · Heat Transfer Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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