上海交通大学学报

上海交通大学学报（自然版）

• 工程力学 • 上一篇    下一篇

基于小波分析的湍流采样数据量缩减算法

张斌1，王彤1，谷传纲1，戴正元2   

  1. (1. 上海交通大学 动力机械及工程教育部重点实验室，上海 200240；
    2. 特灵空调亚太研发中心，上海 200001)
  • 收稿日期:2008-01-09 修回日期:1900-01-01 出版日期:2008-11-28 发布日期:2008-11-28
  • 通讯作者: 王彤

Algorithm of Reducing the Sample Size of Turbulent Experiment Based on Wavelet Analysis

ZHANG Bin1,WANG Tong1,GU Chuan-gang1,DAI Zheng-yuan2   

  1. (1. Key Laboratory for Power Machinery and Engineering (the Ministry of Education)， Shanghai Jiaotong
    University, Shanghai, 200240, China； 2. Trane’s AsiaPacific Research Center, Shanghai 200001, China)
  • Received:2008-01-09 Revised:1900-01-01 Online:2008-11-28 Published:2008-11-28
  • Contact: WANG Tong

摘要： 根据缩减数据必须反映与原数据统计同等的湍流流动信息准则，利用小波分析良好的时频双局域性信号处理特点，结合统计检测理论提出了一种相对合理的湍流采样数据量缩减算法.与传统算法及已有算法比较，由该算法缩减所得的数据量稍大但更能合理反映与原数据统计同等的湍流流动信息.选取湍动能为统计特征量，对沟槽壁面减阻机制实验数据进行了缩减分析，结果验证了该数据缩减算法的合理性和可靠性.

关键词: 湍流采样, 数据缩减, 小波分析, 统计检测

Abstract: Based on the statistic detecting methods, a reasonable algorithm was put forward to reduce the sample size of turbulent experiment. Wavelet analysis method was adopted in the algorithm to get the characteristic parameters of turbulent flow in both time domain and frequency domain. Comparing with the former algorithms, the reduced data size by the algorithm is larger, but it includes the same information with the initial sample data statistically. An example was provided to prove the reliability and rationality of the algorithm, where the sample data is from the experiment on the mechanism of riblets drag reduction and the turbulent kinetic energy is selected as the statistic characteristic parameter.

中图分类号: