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首页> 外文期刊>Experiments in Fluids: Experimental Methods and Their Applications to Fluid Flow >A comparative study of the MQD method and several correlation-based PIV evaluation algorithms
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A comparative study of the MQD method and several correlation-based PIV evaluation algorithms

机译:MQD方法与几种基于相关性的PIV评估算法的比较研究

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The Minimum Quadratic Difference (MQD) method is compared with methods conventionally used for the evaluation of PIV recordings, i.e. correlation-based evaluation with fixed interrogation windows (auto- or cross-correlation) and correlation-based tracking. The comparison is performed by studying the evaluation accuracy achieved when applying these methods to pairs of synthetic PIV recordings for which the true displacements are known. The influence of the magnitude of the particle image displacement, evaluation window size, density of particle image distribution, and particle image size on the accuracy are investigated. In all these cases the best results in terms of a statistical error are obtained with the MQD method. The superiority of the MQD method can be explained can be explained with its potential of accounting far non-uniformities in the particle image distribution and a nonuniform illumination. It is also shown that the conventional correlation-based methods may produce principal errors that are non-existent for the MQD method. The evaluation speed achievable for the MQD method by making use of the FFT is comparable to that common for the generally used auto; or cross-correlation algorithm. Finally, a quantitative explanation is given for the often observed phenomenon that PIV velocity results tend to be smaller than the true values. [References: 21]
机译:将最小二次方差(MQD)方法与传统上用于PIV记录评估的方法进行了比较,即使用固定的查询窗口(自相关或互相关)和基于相关的跟踪进行基于相关的评估。通过研究将这些方法应用于成对的真实位移已知的合成PIV记录对时的评估精度来进行比较。研究了粒子图像位移的大小,评估窗口大小,粒子图像分布的密度和粒子图像大小对精度的影响。在所有这些情况下,使用MQD方法可获得最佳的统计误差结果。可以解释MQD方法的优越性,因为它具有解决粒子图像分布中的远非均匀性和照明不均匀性的潜力。还表明,传统的基于相关性的方法可能会产生MQD方法不存在的主要误差。通过使用FFT,MQD方法可获得的评估速度与通用自动评估系统相当。或互相关算法。最后,对经常观察到的现象给出了定量解释,即PIV速度结果往往小于真实值。 [参考:21]

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