大数据分析方法在风洞试验中的应用

谢艳; 李平; 蒋鸿

doi:10.7638/kqdlxxb-2018.0105

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

大数据分析方法在风洞试验中的应用

研究论文 | 更新时间：2026-05-28

- 大数据分析方法在风洞试验中的应用
- Application of big data analytics approach in wind tunnel test
- 空气动力学学报 2019年37卷第6期页码：1004-1009
- 作者机构：
  
  中国空气动力研究与发展中心,四川,绵阳,621000
- 作者简介：
  
  谢艳(1963-), 女, 四川绵阳人, 高级工程师, 研究方向:风洞测量.E-mail:huier98435936@163.com
- 基金信息：
- DOI：10.7638/kqdlxxb-2018.0105
  中图分类号： V211.7
- 纸质出版：2019
- 稿件说明：
移动端阅览
谢艳, 李平, 蒋鸿. 大数据分析方法在风洞试验中的应用[J]. 空气动力学学报, 2019, 37(6): 1004-1009.

XIE Yan, LI Ping, JIANG Hong. Application of big data analytics approach in wind tunnel test[J]. 2019, 37(6): 1004-1009.
谢艳, 李平, 蒋鸿. 大数据分析方法在风洞试验中的应用[J]. 空气动力学学报, 2019, 37(6): 1004-1009. DOI： 10.7638/kqdlxxb-2018.0105.

XIE Yan, LI Ping, JIANG Hong. Application of big data analytics approach in wind tunnel test[J]. 2019, 37(6): 1004-1009. DOI： 10.7638/kqdlxxb-2018.0105.

摘要

针对风洞常规试验采用阶梯抽样采集的方法，得到的试验数据和信息较少，导致试验数据分析和试验故障分析困难的问题，尝试在风洞常规试验中构建起风洞试验大数据的采集、收集和分析处理平台，并利用大数据较强的洞察能力，助力风洞试验中的疑难问题的分析。主要通过将风洞采集方法改为连续采集试验全程数据，开发杂混数据的通用风洞试验数据处理程序，开发海量试验数据分析显示软件等步骤，搭建起风洞试验大数据综合处理系统。并通过此平台对风洞试验大数据进行挖掘计算，使隐含的有用信息显现出来，为试验数据和试验故障深入分析指明方向。该平台在2 m量级的高速风洞试验中的应用表明，此系统实现了风洞试验全程全部试验数据信息的采集，实现了风洞试验大数据的处理分析和结果展示。通过大数据分析有助于快速理清常规试验中的疑难问题。通过对风洞传统采集、处理方法的改进，实现了风洞试验从传统的阶梯抽样采集的小数据时代到采集全部试验数据信息的大数据时代的转变。从风洞试验大数据中获取的频率、概率、相关关系等数据可以为故障的定位分析、事件因果关系的分析等提供有力的数据支持。

Abstract

The traditional wind tunnel test adopts conventional pitch-and-pause testing mode and get less test data and information

which leads to difficulties in the analysis of test data and analysis of test failures. It is attempted to establish acquisition

analysis

and processing platform of big data for wind tunnel tests and make use of the powerful insights of big data to help analyze the difficult problems in wind tunnel tests. The wind tunnel test big data synthesis processing system can be built mainly through changing the acquisition method of wind tunnel to the method of continuous collection of all test data throughout the test

by developing general wind tunnel test data processing program for hybrid data and massive test data analysis and display software and other steps. Through this platform

excavation calculation is performed on large-scale wind tunnel test data

so that useful and implicit information is revealed

and the direction for in-depth analysis of test data and experimental failures is pointed out. The application of the system in 2 meter-scale high-speed wind tunnel test indicates that the platform has achieved the entire collection of wind tunnel test data

processing and analysis of wind tunnel test big data

and results display. The analysis results can help us quickly clarifying many issues that have troubled us. Through the improvement of conventional collection and process methods for wind tunnel test data

the wind tunnel test has changed from the traditional small-data acquisition of pitch-and-pause sampling to the big data era of collecting all test data information. The frequency

probability

correlation

etc. obtained from wind tunnel test big data can provide powerful data support for fault location analysis and event causality analysis.

关键词

Keywords

references

XIE Y, LI P, WANG R B, et al. An experiment research on dsp technology in wind tunnel[C]//2010 International Conference on Apperceiving Computing and Intelligence Analysis Proceeding, 2010.

LI P, RUI W, QIN J H, et al. Development of measurement and operation management system in 2 m×2 m supersonic wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2012, 26(4): 96-99.

XIE Y, LI P, JIANG H, et al. The key technique research on continuous sweeping angle of attack test in 2.4 m×2.4 m transonic wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2014, 28(1): 89-93.

LU C Q. Big data[M]. Beijing: Beijing University of Technology Press, 2016. (in Chinese)

BAKER P.大数据策略[M].于楠, 译.北京: 清华大学出版社, 2016.

WANG W Z, ZHENG K P, CHEN G, et al. Application research of data-mining to flight test data analysis and aerodynamic parameter identification[J]. Acta Aerodynamica Sinica, 2016, 34(6): 778-782. (in Chinese)

YUAN B N, HUO Z H, BAI X X. Technology development and prospects of big data in flight test[J]. Computer Measurement Control, 2016, 34(6): 778-782. (in Chinese)

李平.一种风洞试验数据采集的方法: 中国, 201318003324.7[P]. 2015-02-04.

ZHU M. Data mining[M]. Hefei: Press of University of Science and Technology of China, 2008. (in Chinese)

WANG M X. Survey of data mining[J]. Software Uuide, 2013, 12(10): 135-137. (in Chinese)

JEONG S, SHIMOYAMA K. Review of data mining for multi-disciplinary design optimization[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2011, 225(5): 469-479.

MONROE U A, FREEMAN K, JONES K I. IT data mining tool uses in aerospace[R]. NASA ARC-E-DAA-TN 4694, 2012.

BRYAN M, SANTANU D, KANISHKA B, et al. Discovering anomalous aviation safety events using scalable data mining algorithms[J]. Journal of Aerospace Information Systems, 2014, 11(7): 482-482.

SHINKYU J, KAZUHISA C, SHIGERU O. Data mining for aerodynamic design space[R]. AIAA 2005-5079, 2005.

OYAMA A, VERBURG P C, NONOMURA T, et al. Flow field data mining of pareto-optimal airfoils using proper orthogonal decomposition[R]. AIAA 2010-1140, 2010.

MACKMAN T J, ALLEN C B. Aerodynamic data modeling using adaptive sampling[R]. AIAA 2010-9194, 2010.

WEI Z, XIE Y, WU J Q, et al. Application of continuous sweeping force measuring technology in large intermittent transonic wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2011, 25(4): 99-102. (in Chinese)

AMIRI K, SOLTANI M R, HAGHIRI A. Steady flow quality assessment of a modified transonic wind tunnel[J]. Scientia Iranica Transactions B: Mechanical Engineering, 2013, 20(3): 500-507.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

阻力舵机翼颤振特性仿真与风洞试验

典型高速飞行器滚转失稳特性研究

典型山体地形下低矮房屋风荷载受周边建筑群干扰效应研究

基于DMD方法的旋翼流场分解与重构

基于双气动弹性模型风洞试验的邻近超高层建筑风致干扰效应