Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows

TL;DR

This paper introduces an information-theoretic neural network approach for time-varying mode decomposition in separated aerodynamic flows, revealing causality and structures in complex flow scenarios without prior knowledge.

Contribution

It presents a novel neural network-based method for extracting causality-driven, time-varying flow structures from aerodynamic data, applicable to both numerical and experimental cases.

Findings

Identifies informative vortical structures linked to lift response.

Reveals how gust effects emerge in lift over time.

Highlights structures near vortex cores in turbulent wakes.

Abstract

We perform an information-theoretic mode decomposition for separated aerodynamic flows. The current data-driven approach based on a neural network referred to as deep sigmoidal flow enables the extraction of an informative component from a given flow field snapshot with respect to a target variable at a future time stamp, thereby capturing the causality as a time-varying modal structure. We consider four examples of separated flows around a wing, namely, 1. laminar periodic wake at post-stall angles of attack, strong gust-wing interactions of 2. numerical and 3. experimental measurements, and 4. a turbulent wake in a spanwise-periodic domain. The present approach reveals informative vortical structures associated with a time-varying lift response. For the periodic shedding cases, the informative structures vary in time corresponding to the fluctuation level from their mean values. With the examples of gust-wing interactions, how the effect of gust on a wing emerges in the lift response over time is identified in an interpretable manner. Furthermore, for the case of turbulent wake, the present model highlights structures near the wing and vortex cores as informative components based solely on the information metric without any prior knowledge of aerodynamics and length scales. This study provides causality-based insights into a range of unsteady aerodynamic problems.