Loss of particle ratio metric for particle image velocimetry accuracy estimation
DOI:
https://doi.org/10.14311/AP.2025.65.0420Keywords:
particle image velocimetry, accuracy, metric, synthetic tests, uncertaintyAbstract
This paper presents an algorithm for evaluating measurement uncertainty at individual points within the Particle Image Velocimetry (PIV) method. The algorithm presents a novel correlation plane metric known as the Loss of Particle Ratio (LPR). This metric is computed by evaluating the magnitude of two correlation peaks: Mutual Information (MI) and the autocorrelation peak. LPR is defined as the ratio of MI, accounting for the total number of particles contributing to signal peak growth, to the magnitude of the autocorrelation peak, which represents the total number of particles within an interrogation area (IA). The computation of LPR allows both the overall measurement accuracy and the accuracy in each direction to be determined. To improve accuracy, the proposed metric undergoes corrections based on the resultant displacement from the last iteration of the Standard Cross-Correlation (SCC) algorithm and the gradient value within the IA. The process of determining the measurement uncertainty relies on the analysis of synthetic data and the application of two tests – the Uniform Flow Test (UFT) and the Couette Flow Test (CFT). The paper explores the impact of individual corrections on the metric and establishes dependencies between the adjusted metric values and measurement uncertainty. The procedure defines the measurement uncertainty based on synthetic test parameterisation, considering key parameters that influence accuracy, such as the density of particles within the IA, the velocity gradient, the particle diameter, the displacement in the last iteration, and the noise level. The synthetic test parametrisation employs various methods for defining the gradient within the IA. The proposed procedure for determining the measurement uncertainty, utilising the corrected metric Loss of Particle Ratio, is compared with an approach based on synthetic test parameterisation for the Standard Cross-Correlation algorithm. The study contributes insights into the effectiveness of the proposed algorithm in assessing measurement uncertainty, offering a comprehensive comparison with existing methodologies.
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Copyright (c) 2025 Jan Novotný, Ludmila Nováková, Ilona Machovská, Miloš Kašpárek
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