The rebound characteristics of 100–500μm quartz particles from an aluminum surface were imaged using the particle shadow velocimetry (PSV) technique. Particle trajectory data were acquired over a range of impact velocity (30–90 m/s) and impact angle (20°–90°) typical for gas turbine applications. The data were then analyzed to obtain coefficients of restitution (CoR) using four different techniques: (1) individual particle rebound velocity divided by the same particle’s inbound velocity (2) individual particle rebound velocity divided by inbound velocity taken from the mean of the inbound distribution of velocities from all particles (3) rebound velocity distribution divided by inbound velocity distribution related using distribution statistics and (4) the same process as (3) with additional precision provided by the correlation coefficient between the two distributions. It was found that the mean and standard deviation of the CoR prediction showed strong dependence on the standard deviation of the inbound velocity distribution. The two methods that employed statistical algorithms to account for the distribution shape [methods (3) and (4)] actually overpredicted mean CoR by up to 6% and CoR standard deviation by up to 100% relative to method (1). The error between the methods is shown to be a strong (and linear) function of correlation coefficient, which is typically 0.2–0.6 for experimental CoR data. Non-Gaussianity of the distributions only accounts for up to 1% of the error in mean CoR, and this largely from the non-zero skewness of the inbound velocity distribution. Particle rebound data acquired using field average techniques that do not provide an estimate of correlation coefficient are most accurately evaluated using method (2). Method (3) can be used with confidence if the standard deviation of the inbound velocity distribution is less than 10% of the mean velocity, or if a linear correction based on an assumed correlation coefficient is applied.

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