Particle streak velocimetry using ensemble convolutional neural networks

This study reports an approach and presents its open-source implementation for quantitative analysis of experimental flows using streak images and convolutional neural networks (CNN). The latter are applied to retrieve a length and an angle from streaks, which can be used to deduce kinetic energy and directionality (up to an 180 circle ambiguity) of an imaged flow. We developed a quick method for generating essentially unlimited number of training and validation images, which enabled efficient training. Additionally, we show how to apply an ensemble of CNNs to derive a formal uncertainty on the estimated quantities. The approach is validated on the numerical simulation of a convective turbulent flow and applied to a longitudinal libration flow experiment. [GRAPHICS] .