Error reduction for time-resolved PIV data based on Navier–Stokes equations
Error reduction for time-resolved PIV data based on Navier–Stokes equations
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DOI：
10.1007/s00348-018-2605-1
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发表时间：
2018-09
影响因子：
2.4
通讯作者：
Hongping Wang;Q. Gao;Shi-Zhao Wang;Yu-Hang Li;Zhongyi Wang;Jinjun Wang
中科院分区：
文献类型：
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作者：
Hongping Wang;Q. Gao;Shi-Zhao Wang;Yu-Hang Li;Zhongyi Wang;Jinjun Wang
在粒子图像测速仪(PIV)中，测量速度的后处理是减小误差和预测流场信息缺失的关键步骤。在这项工作中，时间分辨的PIV数据与不可压缩的N-S N-Stokes方程相结合，以减小测量误差，提高精度。在求解N-S方程时，采用了基于投影法的压力修正格式，并引入了一种优化算法来平衡数值解与数值解之间的保真度。用于PIV数据的PCS，称为PIV-PCS，它不仅可以减小速度散度和压力梯度旋度的误差，而且可以确保流场满足动量方程施加的动力学约束。定义了一个平衡速度修正水平与控制方程残差的重要权重系数，并进行了数值计算。提供了一种最优化的方法。新方法通过两个时间分辨PIV实验进行了评估：一个是关于低雷诺数下圆柱尾流的时间分辨PIV实验，另一个是关于高雷诺数半球三维尾流的层析PIV实验。所有的数值评估和实验应用都与无散度差平滑(DFS)方法进行了比较。结果表明，PIV-PCS方法在减小测量误差和恢复真实物理流动结构方面优于DFS方法。
The post-processing of the measured velocity in particle image velocimetry (PIV) is a critical step in reducing error and predicting missing information of the flow field. In this work, time-resolved PIV data are incorporated with the incompressible Navier–Stokes (N–S) equations to reduce the measurement error and improve the accuracy. A pressure correction scheme (PCS) based on the projection method is adopted to solve the N–S equations, and an optimization algorithm is introduced to balance the fidelity between the PIV data and the numerical solutions. The PCS for PIV data, called PIV–PCS, cannot only reduce the errors in the velocity divergence and the curl of the pressure gradient but also ensure that the flow field satisfies the dynamic constraints imposed by the momentum equation. An important weight coefficientsthat balances the level of the velocity modification with the residual of the governing equation is defined and numerically assessed. A method for optimizing the value ofsis provided. The new approach is evaluated by two time-resolved PIV experiments: one on the 2D wake flow of a circular cylinder at low Reynolds number and one on tomographic PIV for the 3D wake flow of a hemisphere at high Reynolds number. All the numerical assessments and experimental applications are compared with the divergence-free smoothing (DFS) method. The results indicate that the presented PIV–PCS method is superior to the DFS method in terms of reducing the measurement error and recovering the real physical flow structures.