FlowFit3: Efficient data assimilation of LPT measurements

Abstract

Lagrangian particle tracking (LPT, (Schröder & Schanz, 2023)) techniques such as Shake-The-Box (Schanz et al., 2016) provide accurate flow measurements by following the movement of tracer particles within a flow. The technique gives measured values of position, velocity and acceleration at the locations of the scattered particles as opposed to a Eulerian grid like with window based techniques such as PIV. This is advantageous for e.g spatially highly resolved flow statistics. Often it is also desirable to have access to a grid-structured representation of the data, especially for the calculation of spatial gradients of flow field measures. While a simple interpolation onto a grid is possible, data assimilation techniques using physics based regularization such as FlowFit2 (Gesemann et al., 2016) or VIC+ (Schneiders & Scarano, 2016) and VIC# (Jeon et al., 2022) are frequently used nowadays. Through knowledge of the Navier-Stokes equations a higher spatial resolution can be obtained than from interpolation alone (see e.g. Zhou et al. (2024)). We present the new and highly efficient FlowFit3 approach and its evaluation on synthetic data.