Density reconstruction from schlieren images through Bayesian nonparametric models
This addresses the challenge of accurate density reconstruction in fluid dynamics and aerodynamics, offering a novel approach for researchers and engineers in these fields.
The study tackled the problem of extracting quantitative density information from schlieren images by proposing a new method using a scaled, derivative enhanced Gaussian process model, achieving true density estimates from images with horizontal and vertical knife-edge orientations.
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.