Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields Using Convolutional Neural Networks

Abstract

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. For instance, it could be used in the identification of coherent structures in turbulent flows, a hindrance in the ongoing effort to establish a link between coherent structures and turbulence statistics.

A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network

“…A novel PolyCube maps-based parametrization was adopted to instantly calculate the nonlinear response of the flow using a Gaussian process regression. Ströfer et al 6 used convolutional neural network to identify the features in fluid flow. This attempt received good results, which provides a general method to flow feature identification study even for distinguishing between similar ones.…”

Using machine learning to detect the turbulent region in flow past a circular cylinder

“…In some situations, the point-based method may produce small 'patches' (unphysical small turbulent regions in non-turbulent flow, see appendix A). Another type of method that is potentially useful for flow identification is a region-based one, which is used by Ströfer et al (2019) to identify the vortices in the wake of an airfoil. The region-based method treats flow structures as objects, and as such it does not generate the 'patches'.…”

“…Another type of method that is potentially useful for flow identification is a region-based one, which is used by Ströfer et al. (2019) to identify the vortices in the wake of an airfoil. The region-based method treats flow structures as objects, and as such it does not generate the ‘patches’.…”

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“…Deep neural networks (DNNs) has not only achieved great successes in computer vision, natural language processing and other machine learning tasks [19,28], but also captured great attention in the scientific computing community due to its universal approximating power, especially in high dimensional spaces [46]. It has found applications in the context of numerical solution of ordinary/partial differential equations, integraldifferential equations and dynamical systems [16,20,26,36,41,47].…”