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

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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.