Konferensartikel

This paper presents a framework for front tracking of shock waves from high-speed video recordings. The video was corrupted with noise and artifact that made front tracking a challenging task. To our knowledge, the implementation of Unnormalized Optimal Transport for determination of velocity is novel. An Unnormalized Optimal Transport framework was implemented in Python to determine the route of propagation from the recorded high-speed video. The obtained route was further computed for front tracking by two methods; Divergence and Transport method. These methods were investigated with both synthetic images and the recorded high-speed video frames. For both, the Transport method provided better results than the Divergence method. Therefore, the shock wave velocity and the shock angles were calculated from Unnormalized Optimal Transport combined with the Transport method. Preliminary results indicate that our findings are in good agreement with sensor-based measurements. This framework verified that the triple point height is increasing, and the oblique shockwave moves faster than the normal shock wave.

unnormalized optimal transport, front tracking, image processing