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 $$180^{\circ }$$ 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.
Introduction.- Physical and technical background.- Mathematical background of statistical PIV evaluation.- PIV recording techniques.- Image evaluation methods for PIV.- Post-processing of PIV data.- Three-component PIV measurements on planar domains.- Examples of applications.- Bibliography.- Matrices for perspective projection.- Mathematical appendix.- List of symbols.- Index.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.