  An unsupervised machine learning strategy is developed to automatically
cluster the vortex wakes of bio-inspired propulsors into groups of similar
propulsive thrust and efficiency metrics. A pitching and heaving foil is
simulated via computational fluid dynamics with $121$ unique kinematics by
varying the frequency, heaving amplitude, and pitching amplitude. A Reynolds
averaged Navier-Stokes (RANS) model is employed to simulate the flow over the
oscillating foils at $Re=10^6$, computing the propulsive efficiency, thrust
coefficient and the unsteady vorticity wake signature. Using a pairwise Pearson
correlation it is found that the Strouhal number most strongly influences the
thrust coefficient, whereas the relative angle of attack, defined by both the
mid-stroke and maximum have the most significant impact on propulsive
efficiency. Next, the various kinematics are automatically clustered into
distinct groups exclusively using the vorticity footprint in the wake. A
convolutional autoencoder is developed to reduce vortex wake images to their
most significant features, and a k-means++ algorithm performs the clustering.
The results are assessed by comparing clusters to a thrust versus propulsive
efficiency map, which confirms that wakes of similar performance metrics are
successfully clustered together. This automated clustering has the potential to
identify complex vorticity patterns in the wake and modes of propulsion not
easily discerned from traditional classification methods.
