Density reconstruction from schlieren images through Bayesian nonparametric models
Abstract
This study proposes a radically alternate approach for extracting quantitative information from schlieren images. The method uses a scaled, derivative enhanced Gaussian process model to obtain true density estimates from two corresponding schlieren images with the knife-edge at horizontal and vertical orientations. We illustrate our approach on schlieren images taken from a wind tunnel sting model, a supersonic aircraft in flight, and a high-order numerical shock tube simulation.
- Publication:
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arXiv e-prints
- Pub Date:
- January 2022
- DOI:
- 10.48550/arXiv.2201.05233
- arXiv:
- arXiv:2201.05233
- Bibcode:
- 2022arXiv220105233U
- Keywords:
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- Physics - Fluid Dynamics;
- Computer Science - Computer Vision and Pattern Recognition