  Recent efforts have shown machine learning to be useful for the prediction of
nonlinear fluid dynamics. Predictive accuracy is often a central motivation for
employing neural networks, but the pattern recognition central to the network
function is equally valuable for purposes of enhancing our dynamical insight
into confounding dynamics. In this paper, convolutional neural networks (CNNs)
were trained to recognize several qualitatively different subsonic buffet flows
over a high-incidence airfoil, and a near-perfect accuracy was performed with
only a small training dataset. The convolutional kernels and corresponding
feature maps, developed by the model with no temporal information provided,
identified large-scale coherent structures in agreement with those known to be
associated with buffet flows. An approach named Gradient-weighted Class
Activation Mapping (Grad-CAM) was then applied to the trained model to indicate
the importance of these feature maps in classification. Sensitivity to
hyperparameters including network architecture and convolutional kernel size
was also explored, and results show that smaller kernels are better at coherent
structure identification than are larger kernels. A long-short term memory CNN
was subsequently used to demonstrate that with the inclusion of temporal
information, the coherent structures remained qualitatively comparable to those
of the conventional CNN. The coherent structures identified by these models
enhance our dynamical understanding of subsonic buffet over high-incidence
airfoils over a wide range of Reynolds numbers.
