Multi-frame pyramid correlation for time-resolved PIV

By A. Sciacchitano, Fulvio Scarano, Bernhard Wieneke. 2012

165 citations recorded by the source corpus.

A novel technique is introduced to increase the precision and robustness of time-resolved particle image velocimetry (TR-PIV) measurements. The innovative element of the technique is the linear combination of the correlation signal computed at different separation time intervals. The domain of the correlation signal resulting from different temporal separations is matched via homothetic transformation prior to the averaging of the correlation maps. The resulting ensemble-averaged correlation function features a significantly higher signal-to-noise ratio and a more precise velocity estimation due to the evaluation of a larger particle image displacement. The method relies on a local optimization of the observation time between snapshots taking into account the local out-of-plane motion, continuum deformation due to in-plane velocity gradient and acceleration errors. The performance of the pyramid correlation algorithm is assessed on a synthetically generated image sequence reproducing a three-dimensional Batchelor vortex; experiments conducted in air and water flows are used to assess the performance on time-resolved PIV image sequences. The numerical assessment demonstrates the effectiveness of the pyramid correlation technique in reducing both random and bias errors by a factor 3 and one order of magnitude, respectively. The experimental assessment yields a significant increase of signal strength indicating enhanced measurement robustness. Moreover, the amplitude of noisy fluctuations is considerably attenuated and higher precision is obtained for the evaluation of time-resolved velocity and acceleration.

Subfields: Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/feda187053acb34c1edf6238bb2d6c038eb5772e

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In Computational Mechanics

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Citations in the corpus

PIV uncertainty quantification by image matching (2013, methodology): “The accuracy of pyramid correlation has been assessed in a previous article (Sciacchitano et al 2012); typical values of the RMS error below 0.01 pixels were obtained.”
Dense velocity reconstruction from tomographic PTV with material derivatives (2016, methodology): “The PIV result is based on the short-time average cross-correlation (sliding-average correlation, SAC, Sciacchitano et al. 2012) based on four frames, which adds the needed robustness to the analysis in this region of low light intensity.”