Many industrial and fossil fuel energy processes involve two phase flows for mass transit. In order to improve and better understand these two phase flows, knowledge of both the droplet/particle size and spatial distribution are required. The useful diagnostic tools should be able to make in-situ measurements without disturbing the flow field so that undisturbed flow measurements can be obtained. This suggests the need for a novel non-intrusive optical technique that can provide instantaneous measurements of particle size and velocity at multiple spatial points in planar (2-D) fields. Digital Particle Image Velocimetry (DPIV) is being studied as a candidate technique for making these measurements. A Monte-Carlo simulation has been developed that simulates the PIV optical recording system and the electric field scattered from particles in the flow. The simulation incorporates diffraction and the variation in light sheet intensity across the depth of field of the optical system. The simulation also computes the Mie scattered electric fields and images these onto the CCD detector. For small size particles, diffraction effects of the optical system dominate the recorded particle image light intensity distribution on the CCD array, and precludes our ability to determine their size. However, for larger sized particles other effects become more dominant, such as the glare spots in optically clear particles. The simulation allows us to examine the Mie scattered electric field of the particles recorded on the CCD and use the higher order structure in the recorded images to determine particle size. A technique for sizing is proposed which uses the separation between two glare spots visible on the particle image to estimate the particle diameter. The results show that the input particle size distribution to the simulation can be reasonably reproduced using the proposed sizing technique. Analysis of experimental PIV images also demonstrated that this technique is capable of providing moderate accuracy particle size estimates.
Particle Imaging Velocimetry (PIV) is emerging as a powerful measurement technique which can be used as an alternative or complementary approach to Laser Doppler Velocimetry (LDV) in a wide range of research applications. The instantaneous planar velocity measurements obtained with PIV make it an attractive technique for use in the study of the complex flow fields encountered in turbomachinery. The data acquired offer several advantages over traditional LDV data: higher accuracy; multiple measurement points and the ability to study both transient and steady state flow phenomena. Many of the same issues encountered in the application of LDV techniques to rotating machinery apply in the application of PIV. Techniques for optical access, light sheet delivery and particulate seeding are discussed. Preliminary results form the successful application of the PIV technique to a transonic axial compressor are presented.
Particle laden flows are encountered in many fossil fuel and industrial processes such as the gas-solid flows in power plants, liquid-solid flows in coal and grain transportation and gas-liquid flows in combustors. These flows are not well understood, thus computational fluid dynamic modeling of these system has been hampered. Fundamental measurements are required to study the motion of the different phases to obtain an understanding of these flows and to develop reliable models. No reliable non-intrusive methods are available to fully characterize two phase flow fields, i.e. obtain particle size, velocity, concentration and concentration gradients at the same time over an extended area of interest. Intrusive sampling methods compromise the two phase flow field making the measurements suspect. Conventional optical methods do not disturb the flow but provide local point wise velocity measurements only. Particle size can be obtained for only spherical particles using the phase Doppler interferometric methods.
Particle Image Velocimetry provides a means of measuring the instantaneous 2-component velocity field across the planar region of a seeded flow field. In this work only two camera, single exposure images are considered where both cameras have the same view of the illumination plane. Two competing techniques which yield unambiguous velocity vector direction information have been widely used for reducing the single exposure. multiple image data: cross-correlation and particle tracking. Correlation techniques yiedl averaged velocity estimates over subregions of the flow, whereas particle tracking technique give individual particle velocity estimates. The correlation technique requires identification of the correlation peak on the correlation plane corresponding to the average displacement of particles across the subregion. Noise on the images and particle dropout contribute to spurious peaks on the correlation plane, leading to misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work fuzzy logic techniques are used to determine the true correlation displacement peak even when it is not the maximum peak on the correlation plane, hence maximizing the information recovery from the correlation operation, maintaining the number of independent measurements and minimizing the number of spurious velocity vectors. correlation peaks are correctly identified in both high and low seed density cases. The correlation velocity vector map can then be used as a guide for the particle tracking operation. Again fuzzy logic techniques are used, this time to identify the correct particle image pairings between exposures to determine particle displacements, and thus velocity. The advantage of this technique is the improved spatial resolution which is available from the particle tracking operation. Particle tracking alone may not be possible in the high seed density images typically required for achieving good results from the correlation technique. This two staged approach offers a velocimetric technique capable of measuring particle velocities with high spatial resolution over a broad range of seeding densities.
KEYWORDS: Particles, Fuzzy logic, Image processing, Process control, Velocimetry, Control systems, Velocity measurements, Systems modeling, Digital recording, Data processing
Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (> 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each results in more than 200 velocity vectors in under 8 seconds of processing time.
The Surface Tension Driven Convection Experiment (STDCE) is a space transportation system flight experiment to study both transient and steady thermocapillary fluid flows aboard the USML-1 Spacelab mission planned for June 1992. One of the components of data collected during the experiment is a video record of the flow field. This qualitative data is then quantified using an all-electronic, two-dimensional particle image velocimetry (PIV) technique called particle displacement tracking (PDT) which uses a simple space domain particle tracking algorithm. Results using the ground-based STDCE hardware, with a radiant flux heating mode, and the PDT system are compared to numerical solutions obtained by solving the axisymmetric Navier Stokes equations with a deformable free surface. The PDT technique is successful in producing a velocity vector field and corresponding stream function from the raw video data which satisfactorily represents the physical flow. A numerical program is used to compute the velocity vector field and corresponding stream function under identical conditions. Both the PDT system and numerical results were compared to a streak photograph, used as a benchmark, with good correlation.
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