A novel method is introduced for increasing the accuracy and extending the dynamic range of
time-resolved PIV. The approach extends the well-known concept of multiframe particle tracking
velocimetry (PTV) to cross-correlation analysis employed by PIV. The working principle is based on the
determination of fluid element trajectories by tracking their position across an image sequence. The fluid
trajectory correlation (FTC) algorithm deals with the effect of trajectory curvature and non-uniform velocity
during the considered time interval by allowing the motion within the trajectory to be nonlinear. In addition,
the local image deformation accounts for the spatial velocity gradient and its change along the trajectory.
The principle for reduction of the measurement error is threefold: by enlarging the temporal
measurement interval, the relative error becomes smaller; secondly, the random error is reduced with the use
of a least-squares polynomial fitting approach to the individual trajectory; and finally, the use of nonlinear
fitting functions allows for a reduction in truncation errors. The evaluation of velocity proceeds then directly
from the analytical derivatives of the least-squares functions. The principal features of this algorithm are
compared with a single-pair iterative image deformation method through the use of synthetic image
sequences depicting steady flows (solid body rotation and uniform motion), and with an application to an
experimental data set of a submerged circular jet. 德国LaVision PIV/PLIF粒子成像测速场仪 自适应粒子成像测速场仪（PIV） 时间分辨粒子成像测速系统(TR-PIV)
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