Artificial Intelligence in Particle Image Velocimetry

The traditional way to determine or predict the flow of information between particles is to perform the interpolation using mathematical approaches, because this method can get more velocity vectors. Unfortunately, the interpolated vectors are normally unphysical, meaning they fail to satisfy the governing equations of flows and do not represent the physical mechanism of flows. The new artificial intelligence (AI) PIV technique, however, which uses deep learning and convolutional neural networks, promises to end this limitation by generating velocity vector fields with a resolution down to a maximum of 1 pixel.

The adoption of AI in fluid mechanics will continue to evolve and improve. Thousands more images will be added to the training data set to make this AI solution more accurate and even more capable of obtaining results from experimental flows. Beyond the technological breakthrough, real-life applications in academic research and industrial design of the products handling these flows will open new possibilities.

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