TrackFit: Uncertainty Quantification, Optimal Filtering and Interpolation of Tracks for Time-Resolved Lagrangian Particle Tracking

Advanced Lagrangian Particle Tracking methods (such as the STB algorithm (Schanz et al. 2016)) are a very useful tool for uncovering properties of flow. As measurement technique, results such perturbed by different sources errors and noise. This work addresses problem optimal filtering particle tracks well estimating uncertainties derived quantities location, velocity acceleration observed particles. The behavior performance this new method (“TrackFit”), first introduced at Gesemann (2016) is analyzed compared to Savitzky–Golay filter (Savitzky Golay (1964)) which commonly used these purposes. choice parameters uncertainty quantification reconstructed can be extracted from spectral analysis recorded raw tracking data. in contrast where might often driven experience gut feeling. Estimating power density (PSD) trajectory signals purpose parameter selection represents challenge due possibly short signals. In following we will present PSD estimation that applicable scenario. addition, show regardless parameters, resulting not approximate ideal noise reduction unlike “TrackFit” described work.