Convolutional causal learning for aerodynamic flows

TL;DR

This paper introduces a convolutional causal learning method using information-theoretic machine learning and neural networks to analyze and interpret unsteady aerodynamic flows from snapshot data.

Contribution

It combines mode decomposition, neural networks, and autoencoders to identify and interpret causal relationships in complex aerodynamic flow data.

Findings

Successfully applied to vortex-gust interactions and turbulent wakes.

Extracted interpretable effects of gusts on lift responses.

Linked large-scale vortical motion to lift force without spatial scale info.

Abstract

This study aims to capture aerodynamic causality from snapshot data with a time-varying mode decomposition technique referred to as information-theoretic machine learning. The current approach extracts time-dependent informative vortical structures, contributing to the future evolution of the aerodynamic coefficients. The present decomposition is employed with a convolutional neural network, enabling the identification of the spatial continuous mode. In addition, a low-order representation, characterizing the informative vortical structures and their corresponding aerodynamic coefficients, can also be identified by considering autoencoder-based data compression. The present technique is applied to a range of aerodynamic examples, including extreme vortex-gust airfoil interactions, experimentally measured transverse jet-wing interaction, and a turbulent separated wake across different Reynolds numbers. For the cases of gust-wing interaction, the time-varying gust effect on the lift response is extracted in an interpretable manner. With the example of a turbulent wake, the relationship between large-scale vortical motion and lift force is identified without any spatial length-scale information. The proposed approach could serve as a foundation for data-driven causal modeling and control for a range of unsteady flows.