  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.
