Particle image velocimetry
About: Particle image velocimetry is a(n) research topic. Over the lifetime, 14343 publication(s) have been published within this topic receiving 271700 citation(s).
01 Jan 1991-Experiments in Fluids
Abstract: Digital particle image velocimetry (DPIV) is the digital counterpart of conventional laser speckle velocitmetry (LSV) and particle image velocimetry (PIV) techniques. In this novel, two-dimensional technique, digitally recorded video images are analyzed computationally, removing both the photographic and opto-mechanical processing steps inherent to PIV and LSV. The directional ambiguity generally associated with PIV and LSV is resolved by implementing local spatial cross-correlations between two sequential single-exposed particle images. The images are recorded at video rate (30 Hz or slower) which currently limits the application of the technique to low speed flows until digital, high resolution video systems with higher framing rates become more economically feasible. Sequential imaging makes it possible to study unsteady phenomena like the temporal evolution of a vortex ring described in this paper. The spatial velocity measurements are compared with data obtained by direct measurement of the separation of individual particle pairs. Recovered velocity data are used to compute the spatial and temporal vorticity distribution and the circulation of the vortex ring.
01 Jan 1989-
Abstract: One of the most challenging and time-consuming problems in experimental fluid mechanics is the measurement of the overall flow field properties, such as the velocity, vorticity, and pressure fields. Local measurements of the velocity field (i.e., at individual points) are now done routinely in many experiments using hot-wire (HW) or laser velocimetry (LV). However, many of the flow fields of current interest, such as coherent structures in shear flows or wake flows, are highly unsteady. HW or LV data of such flows are difficult to interpret, as both spatial and temporal information of the entire flow field are required and these methods are commonly limited to simultaneous measurements at only a few spatial locations.
16 Oct 2014-Journal of open research software
TL;DR: The accuracy of several algorithms was determined and the best performing methods were implemented in a user-friendly open-source tool for performing DPIV flow analysis in Matlab.
Abstract: Digital particle image velocimetry (DPIV) is a non-intrusive analysis technique that is very popular for mapping flows quantitatively. To get accurate results, in particular in complex flow fields, a number of challenges have to be faced and solved: The quality of the flow measurements is affected by computational details such as image pre-conditioning, sub-pixel peak estimators, data validation procedures, interpolation algorithms and smoothing methods. The accuracy of several algorithms was determined and the best performing methods were implemented in a user-friendly open-source tool for performing DPIV flow analysis in Matlab.
Jerry Westerweel1•Institutions (1)
01 Dec 1997-Measurement Science and Technology
Abstract: The measurement principle of digital particle image velocimetry (PIV) is described in terms of linear system theory. The conditions for PIV correlation analysis as a valid interrogation method are determined. Limitations of the method arise as consequences of the implementation. The theory is applied to investigate the statistical properties of the analysis and to optimize and improve the measurement performance. The theoretical results comply with results from Monte Carlo simulations and test measurements described in the literature. Examples of both correct and incorrect implementations are given.
06 Jul 2005-Experiments in Fluids
TL;DR: The development of the method of particle image velocimetry (PIV) is traced by describing some of the milestones that have enabled new and/or better measurements to be made.
Abstract: The development of the method of particle image velocimetry (PIV) is traced by describing some of the milestones that have enabled new and/or better measurements to be made. The current status of PIV is summarized, and some goals for future advances are addressed.