New insights into particle image velocimetry data using fuzzy-logic-based correlation/particle tracking processing

Abstract

Digital particle image velocimetry (DPIV) data processing has been developed to the point where DPIV image data are processed via auto- or cross-correlation techniques in near real time and the results are displayed on screen as they are processed. Correlation techniques are highly desirable, since they provide velocity measurements on a regular grid, which are readily comparable to CFD predictions of the flow field. In high-speed flows, particle lag effects are always of concern; however, the correlation operation does not provide any means for minimization or elimination of systematic errors in the recorded particle image data. In this paper, we present a combined correlation processing/particle tracking technique providing “super-resolution” velocity measurements. Fuzzy-logic principles are employed to maximize the information recovery in the correlation operation and to determine the correct particle pairings in the tracking operation. The combined correlation/particle tracking technique is applied to DPIV data obtained in the diffuser region of a high-speed centrifugal compressor producing velocity vector maps with an average density of 6 vectors/mm2. Inspection of the particle tracking results revealed large particles that were not following the flow. Using preknowledge of the flow field, the biased velocity estimates arising from large particles in the flow were removed, thereby improving the accuracy of the measurements.