Density reconstruction from schlieren images through Bayesian nonparametric models

Bryn Noel Ubald; Pranay Seshadri; Andrew Duncan

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arXiv · 2201.05233

Density reconstruction from schlieren images through Bayesian nonparametric models

Bryn Noel Ubald·Pranay Seshadri·Andrew Duncan

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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.

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physics.flu-dyn cs.CV

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Bryn Noel Ubald, Pranay Seshadri, Andrew Duncan. 2022-01-13. Density reconstruction from schlieren images through Bayesian nonparametric models. https://arxiv.org/abs/2201.05233

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