A simple trick to improve the accuracy of PIV/PTV data

TL;DR

The paper presents a simple ensemble-based method to reduce systematic errors in PIV/PTV velocity measurements by combining high-resolution mean flow estimates with fluctuating data, validated on simulations and experiments.

Contribution

It introduces a novel ensemble statistics approach to improve PIV/PTV accuracy by merging mean flow and fluctuations, reducing spatial modulation bias.

Findings

Effective reduction of systematic error demonstrated on DNS and experimental data.

Improved velocity field accuracy with minimal additional computational cost.

Applicable to various flow complexities, including turbulent boundary layers.

Abstract

Particle Image Velocimetry (PIV) estimates velocities through correlations of particle images within interrogation windows, leading to a spatial modulation of the velocity field. Although in principle Particle Tracking Velocimetry (PTV) estimates locally a non-modulated particle displacement, to exploit the scattered data from PTV it is necessary to interpolate these data on a structured grid, which implies a spatial modulation effect that biases the resulting velocity field. This systematic error due to finite spatial resolution inevitably depends on the interrogation window size and on the interparticle spacing. It must be observed that all these operations (cross-correlation, direct interpolation or averaging in windows) induce modulation on both the mean and the fluctuating part. We introduce a simple trick to reduce this systematic error source of PIV/PTV measurements exploiting ensemble statistics. Ensemble Particle Tracking Velocimetry (EPTV) can be leveraged to obtain the high-resolution mean flow by merging the different instantaneous realisations. The mean flow can be estimated with EPTV, and the fluctuating part can be measured from PIV/PTV. The high-resolution mean can then be superposed to the instantaneous fluctuating part to obtain velocity fields with lower systematic error. The methodology is validated against datasets with a progressively increasing level of complexity: two virtual experiments based on direct numerical simulations (DNS) of the wake of a fluidic pinball and a channel flow and the experimental data of a turbulent boundary layer. For all the cases both PTV and PIV are analysed.