Measurement of three dimensional velocity profiles in a thin channel flow using Forward Scattering Particle Image Velocimetry (FSPIV)

In order to verify the performance of a new technique which can measure all three components of velocity in a seeded liquid, we have measured the three dimensional velocity profile of a parallel flow of a low viscosity, seeded fluid in a straight channel (6 mm × 48 mm × 0.315 mm). The channel was placed on the stage of a conventional microscope equipped with a long working distance microscope objective, with the narrow dimension along the optical axis (OA). Instead of directly imaging the seed particles, the forward scattered light is recorded from the spherical, polystyrene seed particles (7 μm diameter). Particle velocities in a plane transverse to OA were determined by tracking the centroid of the scattering pattern (as a function of time). To determine the particle's position along the OA, the scattering patterns were compared with a set of calibration patterns collected from the particles at different positions along OA. We also create a set of predicted patterns by combining Mie scattering calculations with the phase transformation of the collecting optics, including wavefront aberrations. Using this technique, which we call Forward Scattering Particle Image Velocimetry (FSPIV), it is possible to easily identify the particle's centroid and to simultaneously obtain the fluid velocity in different planes perpendicular to the viewing direction without changing the collection or imaging optics. This technique does not require a highly coherent source and therefore, the acquired images do not suffer from speckle noise. Additionally, a high density CCD array is sufficient for recording the scattering patterns. The agreement between the calibration patterns and the theoretically predicted patterns is good. We have verified the parabolic velocity profile of the parallel flow within the channel. In our current work, the unknown scattering patterns were correlated with the calibration images, but in the future, we will use a trained neural network to predict a particle's location along the optical axis.