PIV optimization for the study of turbulent flow using spectral analysis

Particle image velocimetry (PIV) is a measurement technique which is well adapted to the study of the structure of turbulent flows, as it allows us to obtain quantitative information on the spatial structure of the velocity field. This contribution presents an experimental approach to characterize the measurement noise of a PIV system and the spatial response of such a method. This approach is based on a specific spectral analysis of the velocity vector field deduced from several PIV experiments. This study was done in two steps. The first step was to measure the noise level of PIV and to determine a model for the PIV transfer function from a series of displacement fields measured in a quiet liquid. This model shows the effect of the interrogation window size and introduces a spectral noise density which is constant for a given recording set-up. The second step was to compute spectra from velocity fields obtained in a turbulent boundary layer in a plane parallel to the wall. These spectra show that PIV behaves as a band pass filter. This series of experiments allows us to build a model for the prediction of the PIV spectrum knowing the real one. This model confirms that the PIV noise is white. It allows us to optimize the interrogation window size in order to obtain the best compromise between the spectral response and the spatial resolution. The rms value of the noise can be estimated from the noise density, allowing us to quantify the measurement accuracy. The improvement of sub-pixel window shift is also discussed, leading to a small decrease in the noise level. An analysis is proposed to identify the main sources of noise: particles cut by the border of the interrogation window, isolated particles, etc.