Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields Using Convolutional Neural Networks (Communications in Computational Physics, 2018)
Feature identification is an important task in many fluid dynamics applicationsand diverse methods have been developed for this purpose. These methodsare based on a physical understanding of the underlying behavior of the flowin the vicinity of the feature. Particularly, they require the definition of suitable criteria(i.e. point-based or neighborhood-based derived properties) and proper selectionof thresholds. However, these methods rely on creative visualization of physical idiosyncrasiesof specific features and flow regimes, making them non-universal andrequiring significant effort to develop. Here we present a physics-based, data-drivenmethod capable of identifying any flow feature it is trained to. We use convolutionalneural networks, a machine learning approach developed for image recognition, andadapt it to the problem of identifying flow features. This provides a general methodand removes the large burden placed on identifying new features. The method wastested using mean flow fields from numerical simulations, where the recirculation regionand boundary layer were identified in several two-dimensional flows througha convergent-divergent channel, and the horseshoe vortex was identified in three-dimensionalflow over a wing-body junction.
Keywords: Machine learning; feature identification; fluid dynamics; convolutional neural network.