The Development of Synthetic Algorithm of Fluid Flow Through Particle Image Velocimetry (PIV)

Particle Image Velocimetry (PIV) provides a whole flow field measurement and it extends over a broad area of application in computing fluid velocity components. Temporal and spatial resolution are two key issues in the analysis of PIV data which often compromises each other.
This report discusses the interrogation performance of a cross-correlation based PIV algorithm with interrogation flexibility. In order to assess the developed algorithm, this procedure is tested with synthetic data for uniform linear flow under various experimental conditions. The most common PIV cross-correlation algorithm, the standard Fast-Fourier Transform (FFT) is evaluated by measuring the displacement of particles in synthetically generated PIV images. By controlling certain image parameters such as particle density, particle diameter, intensity and background noise, measurements uncertainties are reduced, creating ‘ideal’ PIV images for the analysis in this work.
The most crucial parameter that is varied in the synthetic image is the particle shift displacements. Three different flow displacements are imposed between the images i.e. uniform translation, vortex flow and pipe flow.