caa a22 4500 60619889X CHVBK 20210128100936.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00348-015-1911-0 doi (NATIONALLICENCE)springer-10.1007/s00348-015-1911-0 Pressure-field extraction on unstructured flow data using a Voronoi tessellation-based networking algorithm: a proof-of-principle study [Elektronische Daten] [Nathan Neeteson, David Rival] A novel technique is described for pressure extraction from Lagrangian particle-tracking data. The technique uses a Poisson solver to extract the pressure field on a network of data nodes, which is constructed using the Voronoi tessellation and the Delaunay triangulation. The technique is demonstrated on two cases: synthetic Lagrangian data generated for the analytical case of Hill's spherical vortex, and the flow in the wake behind a NACA 0012 which was impulsively accelerated to $$Re = 7{,}500$$ R e = 7 , 500 . The experimental data were collected using four-camera, three-dimensional particle-tracking velocimetry. For both the analytical case and the experimental case, the dependence of pressure-field error or sensitivity on the normalized spatial particle density was found to follow similar power-law relationships. It was shown that in order to resolve the salient flow structures from experimental data, the required particle density was an order of magnitude greater than for the analytical case. Furthermore, additional sub-structures continued to be identified in the experimental data as the particle density was increased. The increased density requirements of the experimental data were assumed to be due to a combination of phase-averaging error and the presence of turbulent coherent structures in the flow. Additionally, the computational requirements of the technique were assessed. It was found that in the current implementation, the computational requirements are slightly nonlinear with respect to the number of particles. However, the technique will remain feasible even as advancements in particle-tracking techniques in the future increase the density of Lagrangian data. Springer-Verlag Berlin Heidelberg, 2015 Neeteson Nathan Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, Canada aut Rival David Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON, Canada aut Experiments in Fluids Springer Berlin Heidelberg 56/2(2015-02-01), 1-13 0723-4864 56:2<1 2015 56 348 https://doi.org/10.1007/s00348-015-1911-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00348-015-1911-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Neeteson Nathan Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, Canada aut NATIONALLICENCE 700 1- Rival David Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 773 0- Experiments in Fluids Springer Berlin Heidelberg 56/2(2015-02-01), 1-13 0723-4864 56:2<1 2015 56 348