Paper Info
Title
Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: A computation and data-driven approach
Abstract
A physics-based data-driven computational framework for the quantitative analysis of vortex kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows are characterized by the dominant influence of a small number of vortex structures, but the complexity of these flows makes it difficult to conduct a quantitative analysis of this influence at the level of individual vortices. The method presented here combines machine learning-inspired clustering methods with a rigorous mathematical partitioning of aerodynamic loads to enable detailed quantitative analysis of vortex kinematics and vortex-induced aerodynamic loads. We demonstrate the utility of this approach by applying it to an ensemble of 165 distinct Navier-Stokes simulations of flow past a sinusoidally pitching airfoil. Insights enabled by the current methodology include the identification of a period-doubling route to chaos in this flow, and the precise quantification of the role that leading-edge vortices play in driving aeroelastic pitch oscillations.
Year
DOI
Venue
2021
10.1016/j.jcp.2021.110515
Journal of Computational Physics
Keywords
DocType
Volume
Fluid-structure interaction,Pitching airfoils,Machine learning,Data-driven methods,Vortex dynamics
Journal
443
ISSN
Citations 
PageRank 
0021-9991
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Karthik Menon100.34
Rajat Mittal217017.59