基于经验模态分解的粒子图像测速流场重构研究

王晓宇 1; 贺静 1; 柯汉兵 2; 林梅 1

doi:10.7652/xjtuxb202101020

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基于经验模态分解的粒子图像测速流场重构研究

更新时间：2025-07-09

- 基于经验模态分解的粒子图像测速流场重构研究
- Reconstruction of Particle Image Velocimetry Flow Field Based on Empirical Mode Decomposition
- 西安交通大学学报 2021年55卷第1期页码：162-169
- 作者机构：
  
  西安交通大学能源与动力工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2020-06-24。作者简介: 王晓宇(1994—),男,硕士生;林梅(通信作者),女,研究员,博士生导师。基金项目: 国家自然科学基金资助项目(51876146)
- DOI：10.7652/xjtuxb202101020
  中图分类号： O357.5
- 网络首发：2021-01-10，
  
  纸质出版：2021
- 稿件说明：
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王晓宇 1, 贺静 1, 柯汉兵 2, 等. 基于经验模态分解的粒子图像测速流场重构研究[J]. 西安交通大学学报, 2021,55(1):162-169.

Reconstruction of Particle Image Velocimetry Flow Field Based on Empirical Mode Decomposition[J]. 2021, 55(1): 162-169.
王晓宇 1, 贺静 1, 柯汉兵 2, 等. 基于经验模态分解的粒子图像测速流场重构研究[J]. 西安交通大学学报, 2021,55(1):162-169. DOI： 10.7652/xjtuxb202101020.

Reconstruction of Particle Image Velocimetry Flow Field Based on Empirical Mode Decomposition[J]. 2021, 55(1): 162-169. DOI： 10.7652/xjtuxb202101020.

摘要

为了消除PIV流场数据中的错误数据并降低误差

利用经验模态分解将PIV流场数据分解为多个本征模态分量

对波形异常的本征模态分量进行滤波处理

并将处理后的本征模态分量与其他波形平滑正常的本征模态分量进行反向叠加

有效实现了流场数据的重构和错误数据的消除。当分解得到的本征模态分量较多且错误数据集中在某一个本征模态分量时

可以通过求解各个本征模态分量与原始流场数据的相关系数

将与原始流场数据相关的本征模态分量进行反向叠加重构并摒弃与原始流场数据不相关的本征模态分量

实现错误数据的直接消除。利用本方法分别对2个人为添加误差为1.7%和3.3%的标准模拟流场进行了处理

处理后的流场数据误差分别为0.002%和0.18%。采用该方法对某实验的原始流场数据进行处理

结果表明错误数据得到了有效消除

流场特性更加清晰准确。本研究可为减小变化缓和的流场数据的误差提供一定的指导。

Abstract

To eliminate the wrong data and reduce error

the particle image velocimetry(PIV)flow field data are decomposed into several intrinsic mode functions by empirical mode decomposition. The flow field data can be reconstructed and wrong data can be eliminated effectively by smoothing the intrinsic mode functions with abnormal waveform and superposing the processed intrinsic mode functions and other intrinsic mode functions with normal waveform. Through calculating the correlation coefficient between each intrinsic mode function and original flow field data

the above process can be simplified as deleting the intrinsic mode functions uncorrelated to original flow field data and superposing other intrinsic mode functions to eliminate the wrong data directly when the decomposed intrinsic mode functions are numerous and the wrong data are concentrated in a certain intrinsic mode function. Two simulated standard flow fields with artificially added errors of 1.7% and 3.3% are processed by the above-mentioned method

and the errors of the two processed flow field data are 0.002% and 0.18%

respectively. The original experimental flow field data are processed by this method. The results show that the wrong data can be effectively eliminated

and the flow field characteristics are more clear and accurate. These research results can be used to reduce error in the tempolabile flow field data.

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