两相流PIV粒子图像处理方法的研究

本文在单相PIV技术的基础上研究了两相流动PIV图像处理方法，采用摸板匹配法和灰度加权标定法对两相粒子进行了识别、区分和标定，采用灰度互相关法对区分后的单相粒子图像进行了处理，应用基于以上方法编制的Windows应用软件，首先对由美国Minnesota大学复杂流动实验室提供的两相流动粒子图片进行了处理，通过对比分析可见，应用本文所采用的方法能对两相粒子进行有效的识别和区分，然后以搅拌槽内液固两相流场为例对此方法进行了应用。     

A new particle tracking algorithm based on deterministic annealing and alternative distance measures

We describe a new particle tracking algorithm for the interrogation of double frame single exposure data, which is obtained with particle image velocimetry. The new procedure is based on an algorithm which has recently been proposed by Gold et al. (Gold et al., 1998) for solving point matching problems in statistical pattern recognition. For a given interrogation window, the algorithm simultaneously extracts: (i) the correct correspondences between particles in both frames and (ii) an estimate of the local flow-field parameters. Contrary to previous methods, the algorithm determines not only the local velocity, but other local components of the flow field, for example rotation and shear. This makes the new interrogation method superior to standard methods in particular in regions with high velocity gradients (e.g. vortices or shear flows). We perform benchmarks with three standard particle image velocimetry (PIV) and particle tracking velocimetry (PTV) methods: cross-correlation, nearest neighbour search, and image relaxation. We show that the new algorithm requires less particles per interrogation window than cross-correlation and allows for much higher particle densities than the other PTV methods. Consequently, one may obtain the velocity field at high spatial resolution even in regions of very fast flows. Finally, we find that the new algorithm is more robust against out-of-plane noise than previously proposed methods. Received: 1 March 1999 / Accepted: 29 July 1999     

Adaptive space resolution for PIV

By using standard particle image velocimetry algorithms the interrogation windows are placed on a structured grid and the spatial resolution is manually chosen. Clearly, a better approach is to choose automatically the processing parameters and to adapt them locally both to the seeding density and flow conditions. An adaptive space resolution method is introduced herein and the performance assessment performed by using both synthetic and real images clearly shows the advantages of the technique.     

Analysis and treatment of errors due to high velocity gradients in particle image velocimetry

This paper deals with errors occurring in two-dimensional cross-correlation particle image velocimetry (PIV) algorithms (with window shifting), when high velocity gradients are present. A first bias error is due to the difference between the Lagrangian displacement of a particle and the real velocity. This error is calculated theoretically as a function of the velocity gradients, and is shown to reach values up to 1 pixel if only one window is translated. However, it becomes negligible when both windows are shifted in a symmetric way. A second error source is linked to the image pattern deformation, which decreases the height of the correlation peaks. In order to reduce this effect, the windows are deformed according to the velocity gradients in an iterative process. The problem of finding a sufficiently reliable starting point for the iteration is solved by applying a Gaussian filter to the images for the first correlation. Tests of a PIV algorithm based on these techniques are performed, showing their efficiency, and allowing the determination of an optimum time separation between images for a given velocity field. An application of the new algorithm to experimental particle images containing concentrated vortices is shown.     

Measured Reynolds stress distributions and energy budgets of a fully pulsed round air jet

 Most particle-tracking velocimetry (PTV) algorithms are not suitable for calculating the velocity vectors of a fluid flow subjected to strong deformation, because these algorithms deal only with flows due to translation. Accordingly, it is necessary to develop a novel algorithm applicable to flows subjected to strong deformations such as rotation, shear, expansion and compression. This paper proposes a novel particle tracking algorithm using the velocity gradient tensor (VGT) which can deal with strong deformations and demonstrates that this algorithm is applicable to some basic fluid motions (rigidly rotating flow, Couette flow, and expansion flow). Furthermore, the performance of this algorithm is compared with the binary image cross-correlation method (BICC), the four-consecutive-time-step particle tracking method (4-PTV), and the spring model particle tracking algorithm (SPG) using simulations and experimental data. As a result, it is shown that this novel algorithm is useful and applicable for the highly accurate measurement and analysis of fluid flows subjected to strong deformations. Received: 9 February 1999/Accepted: 22 November 1999     

Volumetric correlation PIV: a new technique for 3D velocity vector field measurement

A method is proposed that allows three-dimensional (3D) two-component measurements to be made by means of particle image velocimetry (PIV) in any volume illuminated over a finite thickness. The method is based on decomposing the cross-correlation function into various contributions at different depths. Because the technique is based on 3D decomposition of the correlation function and not reconstruction of particle images, there is no limit to particle seeding density as experienced by 3D particle tracking algorithms such as defocusing PIV and tomographic PIV. Correlations from different depths are differentiated by the variation in point spread function of the lens used to image the measurement volume over that range of depths. A number of examples are demonstrated by use of synthetic images which simulate micro-PIV (μPIV) experiments. These examples vary from the trivial case of Couette flow (linear variation of one velocity component over depth) to a general case where both velocity components vary by different complex functions over the depth. A final validation—the measurement of a parabolic velocity profile over the depth of a microchannel flow—is presented. The same method could also be applied using a thick light sheet in macro-scale PIV and in a stereo configuration for 3D three-component PIV.     

A comparative study of five different PIV interrogation algorithms

Five different particle image velocimetry (PIV) interrogation algorithms are tested with numerically generated particle images and two real data sets measured in turbulent flows with relatively small particle images of size 1.0–2.5 pixels. The size distribution of the particle images is analyzed for both the synthetic and the real data in order to evaluate the tendency for peak-locking occurrence. First, the accuracy of the algorithms in terms of mean bias and rms error is compared to simulated data. Then, the algorithms ability to handle the peak-locking effect in an accelerating flow through a 2:1 contraction is compared, and their ability to estimate the rms and Reynolds shear stress profiles in a near-wall region of a turbulent boundary layer (TBL) at Re=510 is analyzed. The results of the latter case are compared to direct numerical simulation (DNS) data of a TBL. The algorithms are: standard fast Fourier transform cross-correlation (FFT-CC), direct normalized cross-correlation (DNCC), iterative FFT-CC with discrete window shift (DWS), iterative FFT-CC with continuous window shift (CWS), and iterative FFT-CC CWS with image deformation (CWD). Gaussian three-point peak fitting for sub-pixel estimation is used in all the algorithms. According to the tests with the non-deformation algorithms, DNCC seems to give the best rms estimation by the wall, and the CWS methods give slightly smaller peak-locking observations than the other methods. With the CWS methods, a bias error compensation method for the bilinear image interpolation, based on the particle image size analysis, is developed and tested, giving the same performance as the image interpolation based on the cardinal function. With the CWD algorithms, the effect of the spatial filter size between the iteration loops is analyzed, and it is found to have a strong effect on the results. In the near-wall region, the turbulence intensity varies by up to 4%, depending on the chosen interrogation algorithm. In addition, the algorithms computational performance is tested.     

A generalized processing technique in digital particle image velocimetry with direct estimation of velocity gradients

A technique is proposed for the processing of digital particle image velocimetry (PIV) images, in one single step providing direct estimates of fluid velocity, out-of-plane vorticity and in-plane shear rate tensor. The method is based on a generalization of the standard PIV cross-correlation technique and substitutes the usual discrete cross-correlation of image pairs with a correlation of interpolated two-dimensional image intensity functions, being subject to affine transformations. The correlation is implemented by using collocation points, on which image intensity values are interpolated. The resulting six-dimensional correlation function is maximized using a general purpose optimization algorithm. The use of the method is demonstrated by application to different types of synthetically generated image pairs constructed with known particle displacement functions. The resulting errors are assessed and compared with those of a representative standard PIV method as well as with those of the present technique using no differential quantities in the search of the peak location. The examples demonstrate that significant improvements in accuracy can be obtained for flow fields with regions containing strong velocity gradients.     

Iterative multigrid approach in PIV image processing with discrete window offset

 The features of an improved algorithm for the interrogation of (digital) particle image velocimetry (PIV) pictures are described. The method is based on cross-correlation. It makes use of a translation of the interrogation areas. Such a displacement is predicted and corrected by means of an iterative procedure. In addition, while iterating, the method allows a refinement of the size of the interrogation areas. The quality of the measured vectors is controlled with data validation criteria applied at each intermediate step of the iteration process. A brief section explains the expected improvements in terms of dynamic range and resolution. The accuracy is assessed analysing images with imposed displacement fields. The improved cross-correlation algorithm has been applied to the measurement of the turbulent flow past a backward facing step (BFS). A systematic comparison is presented with Direct Numerical Simulation (DNS) data available on the subject. Received: 7 October 1997/Accepted: 11 August 1998     

A hybrid digital particle tracking velocimetry technique

A novel approach to digital particle tracking velocimetry (DPTV) based on cross-correlation digital particle image velocimetry (DPIV) is presented that eliminates the need to interpolate the randomly located velocity vectors (typical of tracking techniques) and results in significantly improved resolution and accuracy. In particular, this approach allows for the direct measurement of mean squared fluctuating gradients, and thus several important components of the turbulent dissipation. The effect of various parameters (seeding density, particle diameter, dynamic range, out-of-plane motion, and gradient strength) on accuracy for both DPTV and DPIV are investigated using a Monte Carlo simulation and optimal values are reported. Validation results are presented from the comparison of measurements by the DPTV technique in a turbulent flat plate boundary layer to laser Doppler anemometer (LDA) measurements in the same flow as well as direct numerical simulation (DNS) data. The DPIV analysis of the images used for the DPTV validation is included for comparison. Received: 29 August 1994/Accepted: 31 May 1996