槽道内涡波流场的POD分析

对槽道内涡波流场的瞬态速度矢量场进行了2DPIV测量实验,将2DPIV测量的矢量场数据进行POD分析,根据POD分解的各阶模态的能量比确定了表征涡波流场主导结构的前15阶模态。结果表明,POD分解的前15阶模态发现槽道内涡波流场是由槽道壁面剪切层诱导的涡列以及伴随的波状主流组成;流场中大尺度的涡旋发展为涡对,对波状主流的脉动频率产生影响;根据涡波流场中的驻点和鞍点,获取了流场的大尺度涡对、平均流场以及Helmholtz涡环等明显特征;最后根据POD分解的前15阶模态对槽道内涡波流场进行重组,重组流场表征了槽道内层流状态下波状主流的形态和涡旋共存的涡波结构以及驻点和鞍点的位置处涡旋的变化等主要特征,有效地剔除了PIV测量流场中的随机信息,保留了PIV测量流场的主导特征。     

槽道内涡波流场的POD分析

对槽道内涡波流场的瞬态速度矢量场进行了2DPIV测量实验,将2DPIV测量的矢量场数据进行POD分析,根据POD分解的各阶模态的能量比确定了表征涡波流场主导结构的前15阶模态。结果表明,POD分解的前15阶模态发现槽道内涡波流场是由槽道壁面剪切层诱导的涡列以及伴随的波状主流组成;流场中大尺度的涡旋发展为涡对,对波状主流的脉动频率产生影响;根据涡波流场中的驻点和鞍点,获取了流场的大尺度涡对、平均流场以及Helmholtz涡环等明显特征;最后根据POD分解的前15阶模态对槽道内涡波流场进行重组,重组流场表征了槽道内层流状态下波状主流的形态和涡旋共存的涡波结构以及驻点和鞍点的位置处涡旋的变化等主要特征,有效地剔除了PIV测量流场中的随机信息,保留了PIV测量流场的主导特征。     

水气两相流中稀疏气泡流动速度场的数字图像测量初探

由空压机提供的气体通过—排微小直径的喷嘴进入静止水体，形成水气两相流流场。在单相PIV和PTV技术的基础上，研究稀疏气液两相流情况下气泡的速度场分布。PIV算法采用快速傅立叶互相关分析法，而PTV算法需要获得每幅图像中每个气泡的形心，根据连续图像中的粒子对，计算速度。用PIV和PTV两种算法处理求出气泡的速度并对两种方法进行比较，其最终研究成果可应用于流体及多相流的流量测技术，提高我们进行低密度气液两相流相关研究的测量水平。同时为水气两相流的数值分析和理论研究提供流场测试的数据。     

VISCOUS FLOWFIELD BASED ON DISCONTINUOUS BOUNDARY ELEMENT METHOD AND VORTEX METHOD

基于非协调边界元方法和涡方法的联合应用, 模拟了二维和三维黏性不可压缩流场. 计算中利用离散涡元对漩涡的产生、凝聚和输送过程进行模拟, 并将整体计算域分解为采用涡泡模拟的内部区域和用涡列模拟的数字边界层区域. 计算域中涡量场的拉伸和对流由Lagrangian涡方法模拟, 用随机走步模拟涡量场的扩散. 内部区域涡元涡量场速度由广义Biot-Savart公式计算, 势流场速度则采用非协调边界元方法计算. 非协调边界元将所有节点均取在光滑边界处, 从而避免了法向速度的不连续现象; 而对于系数矩阵不对称的大型边界元方程组,引入了非常高效的预处理循环型广义极小残余(the generalized minimum residual, GMRES)迭代算法, 使得边界元法的优势得到了充分发挥, 同时, 在内部涡元势流场计算中对近边界点采用了正则化算法, 该算法将奇异积分转化为沿单元围道上一系列线积分, 消除了势流计算中速度及速度梯度的奇异性. 二维、三维流场算例证明了所用方法的正确性, 也验证了该算法可以大幅度提高模拟精度和效率.     

粒子图像测速技术互相关算法研究进展

粒子图像测速技术(particle image velocimetry, PIV)中采用的互相关算法就是需要从独立 存在的两幅图像通过一定的判别方法得到流场中各点的流速矢量的计算方法. 互相关算法的具体实现步骤包括图像前处理、区域离散、 匹配原则选取、搜索方法选取和变形预测, 最后对结果进行后处理. 文中从上述几个方面总 结了国内外近年来互相关算法的发展过程, 并通过对各种方法精度和效率的比较对其应用发 展进行了归纳.     

本征正交分解技术在屋盖风压场重建与预测中的应用

给出利用本征正交分解(POD)对屋盖风压场进行重建和预测的研究结果.对一个双坡屋盖用同步多点压力扫描系统进行了风洞试验,根据POD技术采用前若干阶本征模态重建屋盖风压场.采用两种方案预测未布置测压点位置的风压时间序列.第一种方案中利用插值技术获得没有测压点位置的本征模态值.第二种方案对参考屋盖和需预测的新屋盖分别进行试验,结合由参考屋盖试验萃取的本征模态和由新屋盖试验的风压数据计算的主坐标,预测出新屋盖未知区域的风压时间序列.文中对风压场重建和预测的效果作了分析,而且比较了根据测量的风压数据和预测的风压数据所计算的屋盖风致响应.     

Gappy POD方法重构湍流数据的研究

Gappy POD 是一种基于本征正交分解(proper orthogonal decomposition, POD)的数据重构方法. 本文研究了gappy POD在湍流数据重构中的应用, 主要关注了以下两个因素的影响: 第一, 数据本身的复杂程度, 即构成流场的POD模态数量; 第二, 破损区域的面积大小和几何形状. 考虑到上述因素, 本文重新严格地表述了gappy POD的重构过程, 并推导出gappy POD重构误差的公式. 论文选取旋转湍流数据为案例进行了gappy POD重构的研究, 并解释了构成gappy POD重构误差的两个部分. 第一部分来自流场POD展开的截断误差, 该截断误差会被POD基函数在已知点上的值组成的矩阵的最小特征值放大. 这部分误差主要取决于流场的复杂程度, 当流场复杂程度较低时, 相应误差随采用的POD模态数目增大而减小. 当流场复杂程度较高时, 很小的POD截断误差也会导致很大的重构误差, 此时需要采用流场所有的POD模态进行重构以消除截断误差. 重构误差的第二部分来自POD基函数在已知点上的值组成的矩阵的非列满秩性, 它主要取决于破损区域的面积大小和几何形状. 破损区域的面积越大, 或者破损面积相同时, 破损区域内信息所包含的相关性越大, 第二部分的重构误差越大.      

结构非线性动力分析混合时间积分并行算法

综合隐式和显式时间积分技术,对结构非线性动力反应分析提出一种并行混合时间积分算法.该算法采用区域分解技术.将并发性引入到算法中,即利用显式时间积分技术进行界面节点积分而利用隐式算法求解局部子区域.为实现并行混合时间积分算法,设计了灵活的并行数据信息流.编写了该算法的程序,在工作站机群实现了数值算例,验证了算法的精度和性能.计算结果表明该算法具有良好的并行性能,优于隐式算法.     

采用PTV技术研究循环流化床内气固两相流动

采用PTV技术对循环流化床顶部颗粒稀疏流动区域进行了测量，其中采用先进的高速摄像技术获取流动的连续图像，并采用目前有望在气固两相流动测量中发挥较大作用的四种PTV算法：BICC法、VGT法、SPRING法和4-FRAME法，对所获取的图像进行颗粒配对处理，从而得到流场中运动颗粒的速度信息。所得到的结论为：本文所采用的PTV算法在图像处理中都产生少量的伪矢量，通过采取简单的伪矢量识别算法就可以剔除大部分伪矢量；本文实验条件下，测得循环流化床顶部区域内颗粒运动速度差别较小。本文工作为进一步详细实验测量研究奠定了理论与技术基础。     

PIV技术在超及高超声速流场测量中的研究进展

本文分析了超声速流场对测量技术的特殊要求, 归纳了目前将粒子影像测速仪(particle image velocimetry, PIV)技术应用于超声速流场的测量时所面临的主要技术难点以及主要的解决方法, 分析了超声速流场中所用PIV粒子的主要要求、粒子特性、投放方法等, 介绍了PIV技术在超声速、高超声速流场测量中最新的国内外进展, 特别是给出了国内外关于高超声速流场中激波/附面层的相互干扰, 以及高超声速飞行器超燃冲压发动机主要部件内流场的PIV试验研究的最新进展.      

A variational filtration and interpolation technique for PIV employing fluid dynamical constraints

A variational method for post-processing of the velocity fields obtained by particle image velocimetry (PIV) is described. This method allows one to effectively reconstruct the flow field in the areas of the domain where the spurious vectors were discarded either by other filters or manually. If the spurious vectors cannot be removed, they are smoothed in with the surrounding field so that their effect is significantly reduced. The method is based on the application of dynamical constraints such as continuity, smoothness and matching to the original data. The results of the application of the developed algorithm to the velocity fields obtained by PIV in laboratory experiments with quasi-two-dimensional vortex dipoles are discussed.     

POD investigation of the unsteady turbulent boundary layer developing over porous moving flexible fishing net structure

Particle image velocimetry (PIV) measurements are made to investigate the boundary layer developing over a modeled bottom trawl. The random motion of the fishing net structure as well as the flexibility and the porosity of this structure means that it is not enable to access the main characteristics of such a flow, using classical post-processing mathematical tools. An innovative post-treatment tool based on proper orthogonal decomposition (POD) is then developed to extract the mean velocity flow field from each available PIV instantaneous unsteady velocity field. In order to do so, the whole available velocity database is used to compute POD eigenfunctions and the first POD modes are identified as representing the mean flow field. It is then possible to deduce the mean boundary layer flow field for each position of the fishing net structure during PIV measurements. It is then observed that the mean flow field strongly depends on multiple parameters such as surface curvature, structure porosity, random motion of the structure. Streamwise evolution of classical thicknesses of boundary layer flow are also analyzed. The present work also provides benchmark PIV data of the unsteady flow developing on fishing net porous structures, which helps the progress in unsteady numerical codes for this investigation.     

A fast all-in-one method for automated post-processing of PIV data

Post-processing of PIV (particle image velocimetry) data typically contains three following stages: validation of the raw data, replacement of spurious and missing vectors, and some smoothing. A robust post-processing technique that carries out these steps simultaneously is proposed. The new all-in-one method (DCT–PLS), based on a penalized least squares approach (PLS), combines the use of the discrete cosine transform (DCT) and the generalized cross-validation, thus allowing fast unsupervised smoothing of PIV data. The DCT–PLS was compared with conventional methods, including the normalized median test, for post-processing of simulated and experimental raw PIV velocity fields. The DCT–PLS was shown to be more efficient than the usual methods, especially in the presence of clustered outliers. It was also demonstrated that the DCT–PLS can easily deal with a large amount of missing data. Because the proposed algorithm works in any dimension, the DCT–PLS is also suitable for post-processing of volumetric three-component PIV data.     

Vector post-processing algorithm for phase discrimination of two-phase PIV

A simple phase separation method using vector post-processing techniques is evaluated to measure velocity fields in a bubble plume. To provide for validation, fluorescent seeding is used, and two sets of synoptic images are obtained: mixed-phase images containing bubbles and fluorescent particles, and fluid-phase images containing only fluorescent particles. A third dataset is derived by applying a digital mask to remove bubbles from the mixed-phase images. All datasets are processed using cross-correlation particle image velocimetry (PIV). The resulting vector maps for the raw, mixed-phase data contain both bubble and continuous-phase velocity vectors. To separate the phases, a vector post-processing algorithm applies a maximum velocity threshold for the continuous-phase velocities coupled with the vector median filter to identify remaining bubble-velocity vectors and remove them from the mixed-phase velocity field. To validate the phase separation algorithm, the post-processed fluid-phase vectors are compared to PIV results obtained from both the optically separated and digitally masked data. The comparison among these methods shows that the post-processed mixed-phase data have small errors in regions near some bubbles, but for dilute environmental flows (low void fraction and slip velocity approximately equal to the entrained fluid velocity), the algorithm predicts well both instantaneous and time average statistical quantities. The method is reliable for flows having 10% or less of the field of view occupied by bubbles. The resulting instantaneous data provide information on plume wandering and eddy-size distributions within the bubble plume. By comparison among the datasets, it is shown that the patchiness of the vector-post processed and image masked data limit the diameter of identifiable eddy structures to the average distance between bubbles in the image, and that both datasets give identical probability density functions of eddy size. The optically filtered data have better data coverage and predict a greater probability of larger eddies as compared to the other two datasets.     

Three-dimensional vortex wake structure of a flapping-wing micro aerial vehicle in forward flight configuration

The unsteady flow field past a backward-facing step in a rectangular duct is investigated by adopting time-resolved particle image velocimetry (PIV) in the Reynolds number range of 2,640–9,880 based on step height and the inlet average velocity. The PIV realizations are subjected to post-processing techniques, namely, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). At low Reynolds numbers, the second spatial POD modes indicate the presence of the shear layer mode, whereas this feature shifts to higher modes at higher Reynolds numbers. The corresponding temporal modes are Fourier-transformed to obtain the dominant frequency, whose Strouhal number corroborates the above observation. Short-time windows in the transverse velocity component along the shear layer are selected to investigate the temporal stability of the flow field by DMD to quantify the growth rate of the shear layer mode. The higher harmonics of this mode are also observed to grow, albeit at lesser rate. By relating to POD analysis, the most energetic structures were found to correspond to the unstable modes. The correlation between these unstable DMD modes and the Fourier-filtered flow fields for the same frequencies indicate better match for the lower operating Reynolds number case as compared to higher ones. The spatial stability analysis demonstrates the growth of the shear layer vortices, which is combined with the temporal stability analysis to evaluate the phase velocity of the identified shear layer structures. The calculated phase velocity magnitude of the shear layer is found to be reasonably below the local velocity as expected.     

A model-based validation framework for PIV and PTV

The utility of particle image velocimetry (PIV) for measurement of velocity fields in many fluid flows is well established. This has created interest in overcoming difficulties with the technique when applied to increasingly larger fields of view where there exists a significant range of velocities and spatial velocity gradients are large. In this regard, a major deficiency with standard cross-correlation PIV is the inherent link between the density of vectors in the measurement field and the maximum measurable displacement. Several schemes exist to reduce this link. These iterative hierarchical/multiresolution schemes are strongly dependent on vector validation routines to remove spurious vectors. Here the design of a new framework for vector validation is described. This framework is general enough for use with both regular and irregularly spaced vector fields to make it applicable to particle image velocimetry (PIV), particle tracking velocimetry (PTV), and hybrid methods. It is based on the determination of a smoothed displacement field that robustly characterizes the measured field such that invalid vectors are attenuated more than valid vectors. In this particular study a thin-plate spline model is incorporated within an iterative regularized weighted least-squares routine to find a smoothed version of the displacement field that maintains pertinent velocity gradient information. The utility of the methodology is demonstrated for a complex flow profile containing four vortices where the valid displacement ranges from ?1/4 to +1/4 of the area of interest (AOI) dimension. Results indicate that this validation strategy can discriminate between valid and invalid vectors remarkably well over a range of parameter settings. In the example presented a flow field with significant velocity gradients and having a high number of invalid vectors (25%) is accurately validated.     

Flow field decomposition applied to experimental data obtained for a transitional boundary layer

Using experimental data from Particle Image Velocimetry (PIV) measurements, coherent structures of a transitional spatially developing boundary layer are determined. The coherent structures are determined utilizing the Proper Orthogonal Decomposition (POD), which is based on an expansion of the flow field variables into a set of eigenfunctions or modes. For having constant and reproducible flow field conditions, the flow is artificially excited by means of periodic velocity fluctuations. The used excitation device allows the generation of different transition scenarios, where this paper focuses on the case of thefundamental transition. Phase locked excitation signals allow the recording of instantaneous velocity fields of the flow field at certain instants of time. It can be shown that PIV is a suitable technique to provide experimental data for POD. The results of the POD show that already a small number of modes cover most of the kinetic energy of the flow.     

Use of turbulent flow statistical properties for correcting erroneous velocity vectors in PIVUtilisation de l'Estimation Stochastique pour corriger les vecteurs vitesse PIV erronés

For particular turbulent flows, PIV measurements technique provides more than 10% of spurious velocity vectors at each time step. To correct these vectors, we propose to use the Linear Stochastic Estimation (LSE) dealing with the spatial correlation tensor of the velocity. If this tensor cannot be determined in some measurement zones, Proper Orthogonal Decomposition is used to model this tensor. Both reconstruction methodologies are tested from PIV measurements performed in a Spark Ignition engine flow. We show that for coherent structures analysis, the LSE reconstruction method provides better results than classical mathematical interpolation methods. To cite this article: Ph. Druault, Ph. Guibert, C. R. Mecanique 332 (2004).