Performance prediction of point-based three-dimensional volumetric measurement systems

Point-based three-dimensional volumetric measurement systems are defined as multi-view vision systems which reconstruct a three-dimensional scene by first identifying key points on the views and then performing the reconstruction. Examples of these are defocusing digital particle image velocimetry (DDPIV) (Pereira et al 2000 Exp. Fluids 29 S78–84) and 3D particle tracking velocimetry (3DPTV) (Papantoniou and Maas 1990 5th Int. Symp. on the Application of Laser Techniques in Fluid Mechanics) which reconstruct clouds of flow tracers in order to estimate flow velocities. The reconstruction algorithms in these systems are variations of an epipolar line search. This paper presents a generalized error analysis of such methods, both in reconstruction precision (error in the reconstructed scene) and reconstruction quality (number of ambiguities or 'ghosts' produced)

3D particle tracking velocimetry using dynamic discrete tomography

Particle tracking velocimetry in 3D is becoming an increasingly important imaging tool in the study of fluid dynamics, combustion as well as plasmas. We introduce a dynamic discrete tomography algorithm for reconstructing particle trajectories from projections. The algorithm is efficient for data from two projection directions and exact in the sense that it finds a solution consistent with the experimental data. Non-uniqueness of solutions can be detected and solutions can be tracked individually

Bias in particle tracking acceleration measurement

We investigate sources of error in acceleration statistics from Lagrangian Particle Tracking (LPT) data and demonstrate techniques to eliminate or minimise bias errors introduced during processing. Numerical simulations of particle tracking experiments in isotropic turbulence show that the main sources of bias error arise from noise due to position uncertainty and selection biases introduced during numerical differentiation. We outline the use of independent measurements and filtering schemes to eliminate these biases. Moreover, we test the validity of our approach in estimating the statistical moments and probability densities of the Lagrangian acceleration. Finally, we apply these techniques to experimental particle tracking data and demonstrate their validity in practice with comparisons to available data from literature. The general approach, which is not limited to acceleration statistics, can be applied with as few as two cameras and permits a substantial reduction in the spatial resolution and sampling rate required to adequately measure statistics of Lagrangian acceleration

A Feature Tracking velocimetry technique applied to inclined negatively buoyant jets

We have applied a Feature Tracking Velocimetry (FTV) technique to measure displacements of particles on inclined negatively buoyant jets (INBJs), issuing from a circular sharp-edged orifice, in order to investigate, among the others, the symmetry properties of the velocity field on this phenomenon. Feature Tracking Velocimetry is less sensitive to the appearance and disappearance of particles and to high velocity gradients than classical Particle Image Velocimetry (PIV). The basic idea of Feature Tracking Velocimetry is to compare windows only where the motion detection may be successful, that is where there are high luminosity gradients. The Feature Tracking Velocimetry algorithm presented here is suitable in presence of different seeding densities, where other techniques produce significant errors, due to the non-homogeneous seeding at the boundary of a flow. The Feature Tracking Velocimetry algorithm has been tested on laboratory experiments regarding simple jets (SJs) and inclined negatively buoyant jets released from a sharp-edged orifice. We present here velocity statistics, from the first to the fourth order, to study, among the others, the differences between simple jets and inclined negatively buoyant jets, and to investigate how the increase in buoyancy affects the inclined negatively buoyant jet behavior. We remark that, to the best of authors’ knowledge, this is the first attempt to investigate velocity statistics of an order higher than the second on Inclined Negatively Buoyant Jets. Among the others quantities, the mean streamwise velocity decay and the integral Turbulent Kinetic Energy have been measured and analyzed, both along the jet axis and in the upper and lower region of the simple jets and inclined negatively buoyant jets, as well as the streamwise and spanwise velocity skewness and kurtosis evolution along the axis. Results show the role of buoyancy in modifying the inclined negatively buoyant jet features; moreover, it is highlighted that the asymmetry of inclined negatively buoyant jets cannot be considered only a far field feature of this phenomenon, as it arises very close to the release point

General Defocusing Particle Tracking: fundamentals and uncertainty assessment

General Defocusing Particle Tracking (GDPT) is a single-camera, three-dimensional particle tracking method that determines the particle depth positions from the defocusing patterns of the corresponding particle images. GDPT relies on a reference set of experimental particle images which is used to predict the depth position of measured particle images of similar shape. While several implementations of the method are possible, its accuracy is ultimately limited by some intrinsic properties of the acquired data, such as the signal-to-noise ratio, the particle concentration, as well as the characteristics of the defocusing patterns. GDPT has been applied in different fields by different research groups, however, a deeper description and analysis of the method fundamentals has hitherto not been available. In this work, we first identity the fundamental elements that characterize a GDPT measurement. Afterwards, we present a standardized framework based on synthetic images to assess the performance of GDPT implementations in terms of measurement uncertainty and relative number of measured particles. Finally, we provide guidelines to assess the uncertainty of experimental GDPT measurements, where true values are not accessible and additional image aberrations can lead to bias errors. The data were processed using DefocusTracker, an open-source GDPT software. The datasets were created using the synthetic image generator MicroSIG and have been shared in a freely-accessible repository

Event-based imaging velocimetry - An introduction

The presentation provides an introduction to the concepts of event-based imaging (EBI), also known as dynamic vision sensing or neuromorphic imaging. EBI constitutes a pradigm shift in the field of imaging since it does not record typical frame-based image data. Rather, the EBI sensor provides an asynchronous stream of contrast-change events on the pixel level. In the present context the focus is to demonstrate the potentials of EBI in the field of flow visualization and measurement, in particular, particle tracking velocimetry and flow field reconstruction akin to the established particle image velocimetry technique (PIV)