Uncertainty in image-based density measurements
post-template-default,single,single-post,postid-16599,single-format-standard,bridge-core-2.3.9,qode-page-transition-enabled,ajax_fade,page_not_loaded,,qode-title-hidden,qode-theme-ver-29.3,qode-theme-bridge,qode_header_in_grid,wpb-js-composer js-comp-ver-6.2.0,vc_responsive,elementor-default,elementor-kit-16106

Uncertainty in image-based density measurements

We present an uncertainty quantification methodology for density estimation from Background-Oriented Schlieren (BOS) measurements, in order to provide local, instantaneous, a posteriori uncertainty bounds on each density measurement in the field of view. Displacement uncertainty quantification algorithms from cross-correlation-based particle image velocimetry are used to estimate the uncertainty in the dot pattern displacements obtained from cross-correlation for BOS and assess their feasibility. In order to propagate the displacement uncertainty through the density integration procedure, we also develop a novel methodology via the Poisson solver using sparse linear operators. The method allows for instantaneous spatially resolved uncertainty quantification in density estimates from BOS measurements, and for use in CFD model validation and engineering design. A set of Python codes implementing the proposed methodology is made available as open-source software online. 

Published Paper

Data Repository

No Comments

Post A Comment