![]() 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. 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. 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. Recent efforts have shown machine learning to be useful for the prediction of nonlinear fluid dynamics. This work has implications for distinguishing artificial sources of turbulence from natural ones and aiding in identifying the mechanism of turbulence in nature, permitting more accurate mixing models. We find that the network is capable of identifying the correct case with 86% accuracy. We perform numerical simulations of three forms of turbulence-convection, wake, and jet-and then train a convolutional neural network to distinguish between these cases using only a narrow field of view of the velocity field. ![]() This suggests that the inverse is likely also possible: that machine learning can use the properties of turbulence at small scales to identify the nature of the original source and potentially distinguish between different classes of turbulence-generating systems, which is a novel pursuit. This has resulted in the introduction of machine learning techniques to attempt to apply the general body of turbulence simulations to the modeling of turbulence at the subgrid scale. Prior work in turbulence modeling has shown that the fundamental “constants” of turbulence models are often problem-dependent and need to be calibrated to the desired application. ![]() Though turbulence is often thought to have universal behavior regardless of origin, it mayīe possible to distinguish between the types of turbulence generated by different sources. ![]()
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