Lagrangian particle tracking in the presence of obstructing objects

Abstract Volumetric flow measurement techniques have become the state-of-the-art for characterizing a broad range of different flow fields. Still, certain major limitations are present, which hinders the application of these techniques for some of the more complex flow configurations. In particular, flow measurements involving the presence of obstructing objects require time consuming measurement strategies and careful adjustment of the experimental equipment to avoid inaccurate measurement results. Within this study, these limitations are mitigated by the use of a known object’s shape and position in the form of depth maps for commonly used Lagrangian particle tracking schemes like Shake-the-Box (STB) as well as in volume self-calibration methods. The incorporation of these depth maps is computationally inexpensive and straight forward to implement. In order to evaluate the performance of this novel object-aware Lagrangian particle tracking (OA-LPT) approach, synthetic as well as experimental test data is created and the reconstruction quality is evaluated. It is shown, that OA-LPT is capable of providing full flow-field information, whereas the default STB implementation fails to correctly reconstruct particles in the partly-occluded regions.