Nicolas Alexander, Zentgraf Florian, Linne Mark, Dreizler Andreas, Peterson Brian
School of Engineering, Institute of Multiscale Thermofluids, The University of Edinburgh, Edinburgh, UK.
Department of Mechanical Engineering, Reactive Flows and Diagnostics, Technical University of Darmstadt, Darmstadt, Germany.
Exp Fluids. 2023;64(3):50. doi: 10.1007/s00348-023-03594-y. Epub 2023 Feb 21.
The performance of a wavelet-based optical flow velocimetry (wOFV) algorithm in extracting high accuracy and high-resolution velocity fields from tracer particle images in wall-bounded turbulent flows is assessed. wOFV is first evaluated using synthetic particle images generated from a channel flow DNS of a turbulent boundary layer. The sensitivity of wOFV to the regularization parameter ( ) is quantified and results are compared to cross-correlation-based PIV. Results on synthetic particle images indicated different sensitivity to under-regularization or over-regularization depending on which region of the boundary layer is being analyzed. Nonetheless, tests on synthetic data revealed that wOFV can modestly outperform PIV in vector accuracy across a broad range. wOFV showed clear advantages over PIV in resolving the viscous sublayer and obtaining highly accurate estimates of the wall shear stress and thus normalizing boundary layer variables. wOFV was also applied to experimental data of a developing turbulent boundary layer. Overall, wOFV revealed good agreement with both PIV and a combined PIV + PTV method. However, wOFV was able to successfully resolve the wall shear stress and correctly normalize the boundary layer streamwise velocity to wall units where PIV and PIV + PTV showed larger deviations. Analysis of the turbulent velocity fluctuations revealed spurious results for PIV in close proximity to the wall, leading to significantly exaggerated and non-physical turbulence intensity in the viscous sublayer region. PIV + PTV showed only a minor improvement in this aspect. wOFV did not exhibit this same effect, revealing that it is more accurate in capturing small-scale turbulent motion in the vicinity of boundaries. The enhanced vector resolution of wOFV enabled improved estimation of instantaneous derivative quantities and intricate flow structure both closer to the wall and more accurately than the other velocimetry methods. These aspects show that, within a reasonable range that can be verified using physical principles, wOFV can provide improvements in diagnostics capability in resolving turbulent motion occurring in the vicinity of physical boundaries.
评估了基于小波的光流测速(wOFV)算法从壁面湍流中示踪粒子图像提取高精度和高分辨率速度场的性能。首先使用从湍流边界层的槽道流直接数值模拟(DNS)生成的合成粒子图像对wOFV进行评估。对wOFV对正则化参数( )的敏感性进行了量化,并将结果与基于互相关的粒子图像测速(PIV)进行了比较。合成粒子图像的结果表明,根据所分析的边界层区域不同,对欠正则化或过正则化的敏感性也不同。尽管如此,对合成数据的测试表明,在较宽的范围内,wOFV在矢量精度方面略优于PIV。wOFV在解析粘性子层以及获得壁面剪应力的高精度估计从而对边界层变量进行归一化方面,相对于PIV具有明显优势。wOFV还应用于发展中的湍流边界层的实验数据。总体而言,wOFV与PIV以及PIV + 粒子跟踪测速(PTV)组合方法都显示出良好的一致性。然而,wOFV能够成功解析壁面剪应力,并将边界层流向速度正确地归一化为壁面单位,而PIV和PIV + PTV则显示出较大偏差。对湍流速度波动的分析表明,PIV在靠近壁面处会产生虚假结果,导致粘性子层区域的湍流强度显著夸大且不符合物理实际。PIV + PTV在这方面仅略有改善。wOFV没有表现出同样的效果,这表明它在捕获边界附近的小尺度湍流运动方面更准确。wOFV增强的矢量分辨率使得能够比其他测速方法更精确地估计靠近壁面处的瞬时导数量和复杂流动结构。这些方面表明,在可以通过物理原理验证的合理 范围内,wOFV在解析物理边界附近发生的湍流运动的诊断能力方面可以提供改进。