A Reduced-Order Discrete-Vortex Method for Flows with Leading-Edge Vortex Shedding
Pedro Hernandez Gelado, Kiran Kumar Ramesh

TL;DR
This paper introduces a reduced-order discrete vortex method called N-LEV LDVM that accelerates flow simulations with leading-edge vortex shedding by limiting vortex elements, accurately predicting vortex detachment times compared to experiments.
Contribution
The paper proposes a novel N-LEV LDVM method that improves computational efficiency while maintaining accuracy in modeling vortex shedding and detachment in unsteady flows.
Findings
N-LEV LDVM reduces computational cost compared to traditional methods.
The method accurately predicts vortex detachment timing.
Validation against experimental data confirms model effectiveness.
Abstract
The formation of the leading-edge vortex (LEV) is a key feature of unsteady flows past aerodynamic surfaces, but is expensive to model in high fidelity computations. Low-order methods based on discrete vortex elements are able to capture the physical behavior of these flows, in particular when enhanced with a criterion that models the ability of the leading edge to sustain suction. These models are significantly faster than high order methods, but their expense still grows as vortex elements are continuously shed and convected into the wake, in effect an problem. This work proposes accelerating the leading-edge suction parameter discrete vortex method (LDVM) by limiting the number of vortex elements in the LEV coherent structure to N, hence giving the name to the method N-LEV LDVM. The N-LEV LDVM method correctly approximates the flows in comparison with the original…
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