翼型边界层脉动压力统计特征分析

魏斌斌; 高永卫; 孙博; 昔华倩; 邓磊

doi:10.7638/kqdlxxb-2021.0263

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翼型边界层脉动压力统计特征分析

研究论文 | 更新时间：2026-05-28

- 翼型边界层脉动压力统计特征分析
- Statistical characteristics analysis of fluctuating pressure in airfoil boundary layer
- 空气动力学学报 2022年40卷第6期页码：129-137
- 作者机构：
  
  西北工业大学航空学院,西安,710072
- 作者简介：
  
  魏斌斌*（1990-），男，博士，研究方向：实验空气动力学. E-mail：weibinbin123@mail.nwpu.edu.cn
- 基金信息：
  
  航空工业气动院流动显示与测量重点实验室“新风向”联合创新项目（XFX20220502）
- DOI：10.7638/kqdlxxb-2021.0263
  中图分类号： V211;O357.4+1
- 纸质出版：2022
- 稿件说明：
移动端阅览
魏斌斌, 高永卫, 孙博, 等. 翼型边界层脉动压力统计特征分析[J]. 空气动力学学报, 2022, 40(6): 129−137.

WEI Binbin, GAO Yongwei, SUN Bo, et al. Statistical characteristics analysis of fluctuating pressure in airfoil boundary layer[J]. 2022, 40(6): 129-137.
魏斌斌, 高永卫, 孙博, 等. 翼型边界层脉动压力统计特征分析[J]. 空气动力学学报, 2022, 40(6): 129−137. DOI： 10.7638/kqdlxxb-2021.0263

WEI Binbin, GAO Yongwei, SUN Bo, et al. Statistical characteristics analysis of fluctuating pressure in airfoil boundary layer[J]. 2022, 40(6): 129-137. DOI： 10.7638/kqdlxxb-2021.0263.

摘要

为了研究物面边界层流态的脉动特征，使用脉动压力方法对翼型边界层进行了自然转捩探测，深入研究了翼型边界层不同流态的脉动压力统计特征，包括均方根值

、偏度

、陡度

和信息熵

，绘制了不同流态的（

，

）相图。研究表明：层流信息的偏度与Gauss分布的偏度接近，湍流信息的陡度与Gauss分布的陡度接近，而转捩在（

，

）相图上的分布与Gauss分布相差较大。文章详细推导了

与

、

之间的数学关系，建立了

与

、

之间的显式表达式，从而将用于转捩判断的一维决策空间扩展为了四维决策空间（

，

）。研究成果有助于人们深刻理解脉动压力信息熵与一般统计量之间的内在关系，同时对后续的数据分析、分类模型建立以及规则提取等也有一定帮助。

Abstract

To study the fluctuated characteristics of the boundary layer

the transition on an airfoil was detected using the fluctuating pressure method in this paper

and the statistical characteristics of the fluctuating pressure in different flow states

including the root mean square (RMS) value

skewness

kurtosis

and information entropy

were deeply investigated. The (

) phase diagrams of different flow states were presented. The results showed that skewness of the laminar state was close to that of G

auss distribution and kurtosis of the turbulent state was close to that of Gauss distribution

while the distribution in the (

) phase diagram of the transition state was quite different from the Gauss distribution. The mathematical relationship between

and

was deduced in detail and an explicit expression was established

such that the one-dimensional decision space for the transition detection was extended to a four-dimensional decision space (

). This work is useful to deeply understand the internal relationship between the information entropy and the general statistics of fluctuating pressure

and is also of help to subsequent data analysis

classification model establishment and rule extraction.

关键词

Keywords

references

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