Enforcing Temporal Consistency in Physically Constrained Flow Field Reconstruction with FlowFit by Use of Virtual Tracer Particles

Abstract

Processing techniques for particle based optical flow measurement data such as 3D Particle Tracking Velocimetry (PTV) or the novel dense Lagrangian Particle Tracking method Shake-The-Box (STB) can provide time-series of velocity and acceleration information scattered in space. The following post-processing is key to the quality of space-filling velocity and pressure field reconstruction from the scattered particle data. In this work we describe a straight-forward extension of the recently developed data assimilation scheme FlowFit, which applies physical constraints from the Navier-Stokes equations in order to simultaneously determine velocity and pressure fields as solutions to an inverse problem. We propose the use of additional artificial Lagrangian tracers (virtual particles), which are advected between the flow fields at single time instants to achieve meaningful temporal coupling. This is the most natural way of a temporal constraint in the Lagrangian data framework. Not FlowFit's core method is altered in the current work, but its input in form of Lagrangian tracks. This work shows that the introduction of such particle memory to the reconstruction process significantly improves the resulting flow fields. The method is validated in virtual experiments with two independent DNS test cases. Several contributions are revised to explain the improvements, including correlations of velocity and acceleration errors in the reconstructions and the flow field regularization within the inverse problem.