PIV uncertainty quantification is important for comparison with CFD simulations and engineering design, but is challenging due to the complex non-linear measurement chain which involves a large number of parameters. Multiple assessments show that none of the current methods can reliably predict the actual uncertainty...
We propose an improved density integration methodology for Background Oriented Schlieren (BOS) measurements that overcomes the noise sensitivity of the commonly used Poisson solver. The method employs a weighted least-squares (WLS) optimization of the 2D integration of the density gradient field by solving an over-determined...
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...
We present a ray tracing image generation methodology for rendering realistic images of particle image velocimetry (PIV) and background oriented schlieren (BOS) experiments in the presence of density/refractive index gradients. This methodology enables the simulation of experiments for experimental design, error, and uncertainty analysis. Images...
Background-Oriented Schlieren (BOS) is used to measure fluid density from the apparent distortion of a target dot pattern. Here, we propose a new displacement estimation methodology based on tracking individual dots on the pattern as opposed to conventional cross-correlation algorithms. As dot patterns used in...