Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: A computation and data-driven approach

JOURNAL OF COMPUTATIONAL PHYSICS

卷:443

Article

Menon, Karthik¹  Mittal, Rajat¹ 

[1] Johns Hopkins Univ, Dept Mech Engn, Baltimore, MD 21218 USA

关键词: Fluid-structure interaction;    Pitching airfoils;    Machine learning;    Data-driven methods;    Vortex dynamics;   

DOI  :  10.1016/j.jcp.2021.110515

来源: Elsevier

【 摘 要 】

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. (C) 2021 Elsevier Inc. All rights reserved.

【 授权许可】

Free