Influence of Concentration and Number of Image Pairs in μ-PIV Experiments

Abstract

Micro Particle Image Velocimetry (μ-PIV) is a non-intrusive technique widely used nowadays to experimentally obtain the velocity field of a micro flow. The main goal of this research was to examine the influence of particle concentration and the number of images acquired, on the accuracy of the μ-PIV velocity measurement. For this reason, a comparison between experimental and analytical values was made. It has been demonstrated that the influence of the seeding concentration on the accuracy of the velocity measurements, into the investigated range, can be considered insignificant. On the other hand, the number of images selected for the cross-correlation is more important for the accuracy of the measurements. By increasing the quantity of images processed it is possible to artificially increase the seeding concentration and reduce the scatter. However, this considerably increases the processing time for the experiment. A trade-off is required between obtaining a highly accurate result without losing precious experimental down time. When the range of the concentration is fixed, it is possible to set the maximum inaccuracy allowance tolerated for the experiment. There is a compromise between a better precision and adequate time to process the data.

A positivity-preserving adaptive-order finite-difference scheme for GRMHD

“…Nonlinear hybridization has been used to circumvent Godunov's theorem by reconstructing a high-order polynomial using nonlinear combinations of the variables. All modern methods such as essentially non-oscillitary (ENO) [2][3][4][5][6], weighted ENO (WENO) [7,8], and weighted compact nonlinear schemes (WCNSs) [9][10][11] use nonlinear hybridization to achieve higherthan-first-order accuracy. WENO and WCNS schemes are commonly used to construct highorder finite-difference (FD) schemes.…”