Particle image velocimetry (PIV) is an experimental approach widely used to investigate the flow inside stirred tanks. PIV noise sources deteriorate the velocity fields and compromise the measurements, mainly in the estimation of the turbulent parameters. PIV correction approaches based on the primary peak ratio (PPR), namely the ratio between the primary correlation peak to the second highest peak, are used when identifying outliers in the velocity field becomes difficult. However, in some cases, PPR is not sensitive to noise sources and may fail to identify outliers. Therefore, this work proposes a new method of field correction based on mutual information, which is estimated by dividing the height of the cross-correlation peak by the magnitude of the autocorrelation. Analysis were performed using PIV data measured in a 50 L stirred tank. Results show that the proposed methodology can be a valuable tool for the correction of noisy fields.

粒子图像测速 (PIV) 是一种广泛用于研究搅拌罐内流动的实验方法。PIV 噪声源使速度场恶化并影响测量，主要是在湍流参数的估计中。当识别速度场中的异常值变得困难时，使用基于主要峰值比 (PPR) 的 PIV 校正方法，即主要相关峰值与第二高峰之间的比率。但是，在某些情况下，PPR 对噪声源不敏感，可能无法识别异常值。因此，这项工作提出了一种基于互信息的场校正新方法，该方法通过将互相关峰的高度除以自相关的幅度来估计。使用在 50 L 搅拌罐中测量的 PIV 数据进行分析。