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

PIV 图像分析中的自主自适应采样，扩展到多维 PIV 以及扑翼后涡旋脱落的应用

基本信息

批准号：
1812541
负责人：
金额：
--
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1812541
关键词：
Autonomous adaptive sampling PIV image

项目摘要

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.

作为一种实验测量技术，粒子图像测速技术（PIV）允许通过注入在照明时反射光的小粒子来测量空气或水的流速，然后对图像记录进行分析。它的非侵入性的性质，连同其固有的简单性和检索瞬时平面速度测量的能力，使PIV在实验流体相关的动力学领域的一个成熟的，标准化的测量技术，无论是在学术和工业环境中的广泛应用。然而，尽管PIV图像分析程序本身过于简单，但用户需要仔细优化主要处理参数。因此，强加的参数在整个图像中很少是最佳的，并且没有任何迹象表明所获得的结果有多准确。此外，询问区域的位置是独立的基本流动物理限制在获得的速度数据的空间分辨率。测量技术的这些缺点是在过去的几年中得到关注和重视的问题。基于信号密度和速度梯度的自适应性能够以最少的用户输入来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可能性，因为众所周知，该参数固有地调制所获得的位移估计。此外，没有adaptionever被执行利用测量不确定性，虽然这是合乎逻辑的，区域的数据模糊性提高的需求增加sampling.The的目标，在爱德华兹先生的博士学位期间在布里斯托大学进行的工作，因此，将改善自适应图像的PIV分析通过自动耦合流量，信号和误差自适应查询。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，以自动调整相应的询问参数。博士课程将以背景研究开始，以理解现有PIV图像处理算法的概念和实现。在这种情况下，Edwards先生将可以使用他的导师泰尼森博士和艾伦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PIV图像分析设计可靠的曲率几何学。为了避免错误的向量驱动采样，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。可靠性本身可以随后被用来改善抽样策略。一旦获得这些参数，它们就可以结合在PIV图像分析的迭代求解器中。该求解器将自适应地放置和调整可变数量的询问窗口，以最小化计算工作量，同时最大化空间分辨率和精度。换句话说，将自适应地和迭代地优化询问参数。研究结果将使用数值流场和/或实验双组分LDA测量和/或传统和立体PIV测量并列。发展的概念，然后将扩展到多维PIV，从而流动自适应标准可以细化。特别感兴趣的是扩展的概念，立体PIV（3个速度分量在一个平面内）和时间分辨PIV最后，所开发的算法可以应用于机翼涡脱落的流体动力学问题，由于改进的PIV计量学，现在可以更好地表征。由于PIV的广泛使用和所提出的技术的新奇，这项工作在学术界和工业界都具有重要意义和影响。