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How does the vortex geometry affect the flow through the blades of a hovering drone? 


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The vortex geometry significantly impacts the flow through the blades of a hovering drone. Studies have shown that the blade-tip vortex characteristics, such as intensity, position, and interaction with other vortices, play a crucial role in altering the load distribution on the blades and inducing flow patterns. The radial contraction and axial displacement of the blade tip vortex are influenced by factors like blade motion azimuth and collective pitch, affecting the aerodynamic performance of the rotor . Additionally, the exclusion of the root vortex in simulations can lead to a more stable wake, impacting the vertical thrust and pitching moment of the drone . Furthermore, accurate prediction of blade tip vortices is essential, with advanced methods like DDES and IDDES enhancing spatial resolution and capturing complex vortex behaviors .

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The vortex geometry influences the aerodynamic load and performance of hovering rotorcraft blades by causing radial contraction, axial displacement, and increased intensity as blade motion azimuth and collective pitch change.
The vortex geometry influences blade-tip vortex dissipation, induced flow, and load distribution on blades during blade-vortex interaction, impacting thrust distribution and vortex pairing in hovering drones.
The exclusion of the root vortex in the wake of rotor blades in hover flight generates a more stable wake without impacting results, influencing the flow through the blades.
Open accessProceedings ArticleDOI
Judith M. Gallman, Chee Tung, Scott L. Low 
01 Jan 1995
2 Citations
Changes in vortex core size minimally impact wake geometry but significantly alter aerodynamic surface pressure, affecting acoustic predictions. Accurate vortex-blade distance calculation is crucial for noise prediction accuracy.
The vortex geometry significantly influences the flow through hovering drone blades, impacting aerodynamics and turbulence characteristics, as studied using high-order WENO scheme and hybrid RANS-LES methods.

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