科研之友
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摘要
A novel method is introduced for increasing the accuracy and extending the dynamic range of time-resolved particle image velocimetry (PIV). The approach extends the concept of particle tracking velocimetry by multiple frames to the pattern tracking by cross-correlation analysis as employed in PIV. The working principle is based on tracking the patterned fluid element, within a chosen interrogation window, along its individual trajectory throughout an image sequence. In contrast to image-pair interrogation methods, the fluid trajectory correlation concept deals with variable velocity along curved trajectories and non-zero tangential acceleration during the observed time interval. As a result, the velocity magnitude and its direction are allowed to evolve in a nonlinear fashion along the fluid element trajectory. The continuum deformation (namely spatial derivatives of the velocity vector) is accounted for by adopting local image deformation. The principle offers important reductions of the measurement error based on three main points: by enlarging the temporal measurement interval, the relative error becomes reduced; secondly, the random and peak-locking errors are reduced by the use of least-squares polynomial fits to individual trajectories; finally, the introduction of high-order (nonlinear) fitting functions provides the basis for reducing the truncation error. Lastly, the instantaneous velocity is evaluated as the temporal derivative of the polynomial representation of the fluid parcel position in time. The principal features of this algorithm are compared with a single-pair iterative image deformation method. Synthetic image sequences are considered with steady flow (translation, shear and rotation) illustrating the increase of measurement precision. An experimental data set obtained by time-resolved PIV measurements of a circular jet is used to verify the robustness of the method on image sequences affected by camera noise and three-dimensional motions. In both cases, it is demonstrated that the measurement time interval can be significantly extended without compromising the correlation signal-to-noise ratio and with no increase of the truncation error. The increase of velocity dynamic range scales more than linearly with the number of frames included for the analysis, which supersedes by one order of magnitude the pair correlation by window deformation. The main factors influencing the performance of the method are discussed, namely the number of images composing the sequence and the polynomial order chosen to represent the motion throughout the trajectory.
- 出版日期2013-3
