| dc.description.abstract | The determination of flow velocity in complex flows is a fundamental task in
fluid dynamics. PTV tracks the positions of small buoyant seeding particles
through the image sequence to give the full-field velocity. 3D PTV requires
the establishment of correspondences between particles from multiple
images. As the seeding density increases, ambiguities in identifying the
correspondences increase. Multicolour 3D PTV was investigated to reduce
such ambiguities and then increase the spatial resolution.
In this thesis, for the first time mathematical models and numerical simulations
were used to quantify the improvement of multicolour PTV and validated by
experiments. Conclusions were also drawn on the optimal seeding density. It
was concluded that at a certain seeding density, the probability of success
and the optimal seeding density increase obviously when multiple colours
were introduced. For the spatial matching, it was found that by using 5-colour
particles the optimal seeding density increased about 4 times, and at the
optimal seeding density of 5-colour condition, the probability of success was
40 times greater. Similar improvement has been found in the temporal
matching. Then, the measurement of a 3D dynamic flow was described, at a
high seeding density when conventional PTV was not able to produce long
trajectories. | en_US |
