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.

Twenty years of particle image velocimetry

Optical distortion due to inaccurate optical alignment, lens nonlinearity, and/or refraction by optical windows, fluid interfaces, and other optical elements of an experiment causes inaccuracy by introducing variable magnification. Since fractional changes in the magnification have a one-to-one effect on the accuracy of measuring the velocity, it is important to compensate for such distortions. A general experimental calibration procedure is described which determines the magnification matrix of a distorted imaging system, and an algorithm is presented to compute accurate velocity field displacements from measurements of distorted PIV images. These procedures form a basis for generalized stereoscopic PIV procedures which permit easy electronic registration of multiple cameras and accurate recombination of stereoscopic displacement fields to obtain the three-dimensional velocity vector field.

https://iopscience.iop.org/article/10.1088/0957-0233/8/12/008/pdf

Distortion compensation for generalized stereoscopic particle image velocimetry

Distortion Compensation for Generalized Stereoscopic Particle Image Velocimetry

A neural network particle finding algorithm and a new four-frame predictive tracking algorithm are proposed for three-dimensional Lagrangian particle tracking (LPT). A quantitative comparison of these and other algorithms commonly used in three-dimensional LPT is presented. Weighted averaging, one-dimensional and two-dimensional Gaussian fitting, and the neural network scheme are considered for determining particle centers in digital camera images. When the signal to noise ratio is high, the one-dimensional Gaussian estimation scheme is shown to achieve a good combination of accuracy and efficiency, while the neural network approach provides greater accuracy when the images are noisy. The effect of camera placement on both the yield and accuracy of three-dimensional particle positions is investigated, and it is shown that at least one camera must be positioned at a large angle with respect to the other cameras to minimize errors. Finally, the problem of tracking particles in time is studied. The nearest neighbor algorithm is compared with a three-frame predictive algorithm and two four-frame algorithms. These four algorithms are applied to particle tracks generated by direct numerical simulation both with and without a method to resolve tracking conflicts. The new four-frame predictive algorithm with no conflict resolution is shown to give the best performance. Finally, the best algorithms are verified to work in a real experimental environment.

A quantitative study of three-dimensional Lagrangian particle tracking algorithms

Cyclic Variability in Spark Ignition Engines A Literature Survey

A three-dimensional Particle Tracking Velocimetry (3-D PTV) technique has been developed to provide time-resolved, three-dimensional velocity field measurements throughout a finite volume. This technique offers many advantages for fundamental research in turbulence and applied research in areas such as mixing and combustion. The data acquired in 3-D PTV is a time sequence of stereo images of flow tracer particles suspended in the fluid. In this paper, the implementation of the technique is discussed in detail, as well as the results of an extensive statistical investigation of the performance of the algorithms. The technique has been optimized to allow fully automatic processing of long sequences of image pairs in a computationally efficient manner, hereby providing a viable, practical tool for the study of complex flows.

Algorithms for fully automated three-dimensional particle tracking velocimetry

Experimental measurements of burn rates have been carried out in a single cylinger homogeneous charge engine. Three different combustion chambers were investigated (75 % and 60 % squish bowl-in-piston chambers and a disk chamber) using a cylinder head with a swirl producing intake port and near central spark location. Data were obtained with each combustion chamber as a function of spark timing, EGR, and load at 1500 RPM. The combustion rate is strongly influenced by chamber shape. The 10-90 % burn durations of the 75 % and 60 % squish chambers are respectively about 40 % and 60 % that of the disk chamber. Chamber configuration had less effect on 0-10 % burn duration. The disk had about 25 % longer 0-10 % burn time than the bowl-in-piston chambers. Modifications to the GESIM model enabled good overall agreement between predictions and experimental data, a rather severe test of the model because the coupling of fluid mechanics, combustion and chamber geometry must be properly modeled. An improved basic understanding of the influence of combustion chamber shape on burn rate has been achieved through the interactive use of experimental data and modeling. The results suggest that differences in turbulence intensity and flame area development due to changes in chamber shape are responsible for the observed burn rate differences.

Combustion Chamber Effects on Burn Rates in a High Swirl Spark Ignition Engine

Characterization of Intake-Generated Flow Fields in I.C. Engines Using 3-D Particle Tracking Velocimetry (3-D PTV)

An Engine Simulation Model was used to study the effect of in-cylinder swirl level on turbulence intensity and burn rate while holding the inducted kinetic energy constant. Experimental measurements of burn rate for three different swirl levels were obtained and compared with model predictions. The turbulence model used previously did not include wall shear effects and showed little enhancement of turbulence due to swirl, causing small changes in predicted burn rate when the swirl level was changed. An improved turbulence model is proposed which includes production of turbulence due to wall shear effects. Turbulence intensity predictions from the improved model resulted in excellent agreement between the measured and predicted burn rates as swirl level was changed. In addition, the model was used to predict the effect of swirl levels on ISFC. Results showed that ISFC changes were overall small for the range of swirl levels considered.

Modeling the effect of swirl on turbulence intensity and burn rate in S. I. engines and comparison with experiment

Photogrammetry, the measurement of three-dimensional object coordinates from stereo photographicimages, is an essential element of 3-D PTV. Ray tracing is the method that has been used traditionally,however it requires accurate knowledge of image distance, camera locations relative to the object field, andthe size, shape, and refractive index of intervening elements such as apparatus windows. These parametersneed not be known if one uses an optical transform and in situ photogrammetric calibration. The useof this technique significantly reduces the computational time required for 3-D PTV and increases theaccuracy of the measured velocity fields.This technique has been integrated with two others we have recently developed: one is 2-D particletracking using the concept of "path coherence", and the other is stereo matching based on 2-D "particletracks" rather than the particles themselves. In combination, these techniques make 3-D PTV a powerfuland effective tool for advanced research in fluid dynamics and for product development where fluid motionplays a major role in the success of the design, e.g. piston engines.

J. C. Kent

Papers

Algorithms for fully automated three-dimensional particle tracking velocimetry

Combustion Chamber Effects on Burn Rates in a High Swirl Spark Ignition Engine

Characterization of Intake-Generated Flow Fields in I.C. Engines Using 3-D Particle Tracking Velocimetry (3-D PTV)

Modeling the effect of swirl on turbulence intensity and burn rate in S. I. engines and comparison with experiment

Photogrammetric calibration for improved three-dimensional particle-tracking velocimetry (3D PTV)