Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields

TL;DR

This paper introduces a versatile, data-driven approach using convolutional neural networks to automatically identify various fluid flow features without manual criteria tuning, enhancing automation and generality in fluid dynamics analysis.

Contribution

The novel contribution is applying CNNs to fluid flow feature detection, enabling identification of multiple feature types without explicit physics-based criteria or thresholds.

Findings

Successfully identified recirculation regions and boundary layers in 2D flows.

Accurately detected horseshoe vortices in 3D flow simulations.

Method is adaptable to different flow features and flow regimes.

Abstract

Feature identification is an important task in many fluid dynamics applications and diverse methods have been developed for this purpose. These methods are based on a physical understanding of the underlying behavior of the flow in the vicinity of the feature. Particularly, they rely on definition of suitable criteria (i.e. point-based or neighborhood-based derived properties) and proper selection of thresholds. For instance, among other techniques, vortex identification can be done through computing the Q-criterion or by considering the center of looping streamlines. However, these methods rely on creative visualization of physical idiosyncrasies of specific features and flow regimes, making them non-universal and requiring significant effort to develop. Here we present a physics-based, data-driven method capable of identifying any flow feature it is trained to. We use convolutional neural networks, a machine learning approach developed for image recognition, and adapt it to the problem of identifying flow features. The method was tested using mean flow fields from numerical simulations, where the recirculation region and boundary layer were identified in a two-dimensional flow through a convergent-divergent channel, and the horseshoe vortex was identified in three-dimensional flow over a wing-body junction. The novelty of the method is its ability to identify any type of feature, even distinguish between similar ones, without the need to explicitly define the physics (i.e. through development of suitable criterion and tunning of threshold). This provides a general method and removes the large burden placed on identifying new features. We expect this method can supplement existing techniques and allow for more automatic and discerning feature detection. The method can be easily extended to time-dependent flows, where it could be particularly impactful.