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

TL;DR

This paper introduces a data-driven computational framework combining machine learning clustering and mathematical load partitioning to analyze vortex kinematics and aerodynamic loads in vortex-dominated flows, demonstrated on flow past a pitching airfoil.

Contribution

It presents a novel integrated approach for detailed quantitative analysis of individual vortices and their aerodynamic effects in complex flows.

Findings

Identified a period-doubling route to chaos in vortex-dominated flow.

Quantified the influence of leading-edge vortices on aeroelastic pitch oscillations.

Validated the approach on 165 Navier-Stokes simulations.

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.