Details

Autor(en) / Beteiligte

• van Gent, P. L.
• Michaelis, D.
• van Oudheusden, B. W.
• Weiss, P. -É.
• de Kat, R.
• Laskari, A.
• Jeon, Y. J.
• David, L.
• Schanz, D.
• Huhn, F.
• Gesemann, S.
• Novara, M.
• McPhaden, C.
• Neeteson, N. J.
• Rival, D. E.
• Schneiders, J. F. G.
• Schrijer, F. F. J.

Titel

Comparative assessment of pressure field reconstructions from particle image velocimetry measurements and Lagrangian particle tracking

Ist Teil von

• Experiments in fluids, 2017-04, Vol.58 (4), p.1-23, Article 33

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• A test case for pressure field reconstruction from particle image velocimetry (PIV) and Lagrangian particle tracking (LPT) has been developed by constructing a simulated experiment from a zonal detached eddy simulation for an axisymmetric base flow at Mach 0.7. The test case comprises sequences of four subsequent particle images (representing multi-pulse data) as well as continuous time-resolved data which can realistically only be obtained for low-speed flows. Particle images were processed using tomographic PIV processing as well as the LPT algorithm ‘Shake-The-Box’ (STB). Multiple pressure field reconstruction techniques have subsequently been applied to the PIV results (Eulerian approach, iterative least-square pseudo-tracking, Taylor’s hypothesis approach, and instantaneous Vortex-in-Cell) and LPT results (FlowFit, Vortex-in-Cell-plus, Voronoi-based pressure evaluation, and iterative least-square pseudo-tracking). All methods were able to reconstruct the main features of the instantaneous pressure fields, including methods that reconstruct pressure from a single PIV velocity snapshot. Highly accurate reconstructed pressure fields could be obtained using LPT approaches in combination with more advanced techniques. In general, the use of longer series of time-resolved input data, when available, allows more accurate pressure field reconstruction. Noise in the input data typically reduces the accuracy of the reconstructed pressure fields, but none of the techniques proved to be critically sensitive to the amount of noise added in the present test case.