Autonomous adaptive sampling in PIV image analysis with extension to multi-dimensional PIV and application to vortex shedding behind a flapping wing

作为一种实验测量技术，粒子图像测速（PIV）允许通过注入小颗粒来测量空气或水的流速，这些小颗粒在照射时反射光线，然后对图像记录进行分析。其非侵入性以及其固有的简单性和检索瞬时平面速度测量的能力使PIV在学术和工业环境中成为实验流体相关动力学领域成熟的标准化测量技术，具有广泛的应用。然而，尽管PIV图像分析程序本质上是简单的，但用户需要仔细优化主要处理参数。因此，施加的参数在整个图像中很少是最优的，并且没有迹象表明所获得的结果有多准确。此外，审问区域的位置与底层流动物理无关，这限制了获得的速度数据的空间分辨率。这些测量技术的缺陷是近年来引起人们关注和重视的问题。基于信号密度和速度梯度的自适应能够以最小的用户输入最大化测量精度和分辨率。然而，流场的曲率提供了进一步提高精度的潜力，因为众所周知，这个参数固有地调节了获得的位移估计。此外，尽管数据模糊程度高的区域需要增加采样是合乎逻辑的，但从未利用测量不确定性进行过自适应。因此，爱德华兹先生在布里斯托尔大学攻读博士学位期间的工作目标将是通过自动耦合流、信号和误差自适应来改善PIV分析中的自适应图像查询。概念将受到计算流体动力学（CFD）中常见的数值技术的启发，并在递归PIV图像分析例程中实现，以自动相应地调整询问参数。博士课程将以背景研究开始，以了解现有PIV图像处理算法的概念和实现。在这种情况下，爱德华兹先生可以使用由他的导师Theunissen博士和Allen教授在布里斯托尔大学开发的最先进的图像处理代码。该项目将逐步发展为设计可靠的曲率启发法用于PIV图像分析。为了避免错误的矢量驱动采样，曲率估计必须足够可靠。这本身就意味着基于数据可靠性的某种形式的自动适应性。可靠性本身随后可以用来改进采样策略。一旦获得这些参数，它们就可以组合在PIV图像分析的迭代求解器中。该求解器将自适应地放置和大小可变数量的询问窗口，以最大限度地减少计算工作量，同时最大化空间分辨率和精度。换句话说，审讯参数将自适应迭代优化。结果将使用数值流场和/或实验双分量lda测量和/或传统和立体PIV测量并置。然后将开发的概念扩展到多维PIV，从而可以改进流量适应性标准。特别令人感兴趣的是将已开发的概念扩展到立体PIV（单个平面上的3个速度分量）和时间分辨PIV（时间适应性）。最后，所开发的算法可以应用于机翼旋涡脱落的流体动力学问题，由于改进的涡流计量学，现在可以更好地表征机翼旋涡脱落。由于PIV的广泛应用和所提出的技术的新颖性，这项工作将在学术界和工业界都具有很高的意义和影响。

As an experimental measurement technique Particle Image Velocimetry (PIV) allows themeasurement of flow velocity of air or water by injecting small particles which reflect lightwhen illuminated, followed by analysis of the image recordings. Its non-intrusive naturetogether with its intrinsic simplicity and capability of retrieving instantaneous planar velocitymeasurements have made PIV a mature, standardized measurement technique in the fieldof experimental fluid-related dynamics both in academic and industrial environments for awide range of applications. However, although the PIV image analysis procedure isinherently simplistic, the user is required to carefully optimise the dominant processingparameters. Consequently, the imposed parameters will rarely be optimal throughout theimage and no indication is available as to how accurate the obtained results are. Moreover,the location of the interrogation areas is independent of underlying flow physics restrictingthe spatial resolution in obtained velocity data. These drawbacks of the measurementtechnique are problems which have gained concern and attention over the last few years.Adaptivity on the basis of signal density and velocity gradients is able to maximisemeasurement accuracy and resolution with minimal user input. However, curvature of theflow field offers the potential to further improve the accuracy as it is known that thisparameter inherently modulates obtained displacement estimates. Moreover, no adaptationhas ever been performed utilising measurement uncertainty although it is logical thatregions of heightened data ambiguity demand increased sampling.The objective of the work performed during Mr. Edwards' PhD at the University of Bristol willtherefore be to ameliorate the adaptive image interrogation in PIV analyses by automaticallycoupling flow, signal and error adaptivity. Concepts will be inspired by numerical techniquescommon in Computational Fluid Dynamics (CFD) and implemented in recursive PIV imageanalysis routines to automatically adjust interrogation parameters accordingly.The PhD program will be initiated with a background study to comprehend the concept andimplementation of existing PIV image processing algorithms. To this extent Mr. Edwards willhave access to state-of-the-art image processing codes developed at the University ofBristol by his supervisors Dr Theunissen and Prof. Allen. The project will gradually evolveinto devising reliable curvature heuristics for PIV image analysis. To avoid erroneousvectors driving the sampling, curvature estimates must be sufficiently reliable. This in itselfwill imply some form of automated adaptivity based on data reliability. Reliability itself cansubsequently be used to ameliorate the sampling strategy. Once such parameters areobtained, they can be combined in an iterative solver for PIV image analyses. This solverwill adaptively place and size a variable number of interrogation windows as to minimisecomputational effort while maximising spatial resolution and accuracy. In other words,interrogation parameters will be optimised adaptively and iteratively. Findings will bejuxtaposed using numerical flow fields and/or experimental two-component LDAmeasurements and/or traditional and stereoscopic PIV measurements. The developedconcepts will then be extended to multi-dimensional PIV, whereby flow adaptivity criteria canbe refined. Of particular interest is the extension of the developed concepts to stereoscopicPIV (3 velocity components in a single plane) and time-resolved PIV (temporal adaptivity).Finally the developed algorithms can be applied to fluid dynamics problem of vortexshedding of wings, which can now be better characterised due to the improved PIVmetrology.Because of the widespread use of PIV and the novelty of the proposed techniques, the workwill be of high significance and impact both in academia and industry.