.png){kind=icon}
Return
Convolutional causal learning for aerodynamic flows
.png){kind=icon}
OA
AI
DOI：10.1017/jfm.2026.11699.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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; characterising 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 aerofoil 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 modelling and control for a range of unsteady flows.
Keywords:
machine learning
low-dimensional models
vortex interactions
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
2.0W
Citations:
9.4W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSave
View more