连续变迎角试验数据自适应分段拟合滤波方法

王南天; 王雄; 孙鹏; 许晓斌; 邹琼芬

doi:10.13700/j.bh.1001-5965.2020.0145

连续变迎角试验数据自适应分段拟合滤波方法

doi: 10.13700/j.bh.1001-5965.2020.0145

中国空气动力研究与发展中心超高速所, 绵阳 621000

详细信息

通讯作者:
王南天. E-mail: wangnant@126.com

中图分类号: V216.1
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- PDF下载量: 57
- 被引次数: 0
出版历程
- 收稿日期: 2020-04-16
- 录用日期: 2020-07-10
- 网络出版日期: 2021-06-20

Self-adaptive piecewise fitting filtering method for test data in continuous sweeping angle of attack

Hypervelocity Aerodynamics Institute, China Aerodynamics Research & Development Center, Mianyang 621000, China

More Information

Corresponding author: WANG Nantian. E-mail: wangnant@126.com

摘要

摘要:
针对常用的阶梯变迎角数据处理方法处理连续变迎角试验数据时，不能去除低频振动分量的问题，基于试验数据中的近似对称、局部线性等特点，提出一种将数据进行分段二项式拟合的处理方法，并详细论述其基本原理，重点介绍基于方差极小值动态确定数据分段长度的方法。基于测试信号的实验结果表明所提方法可将低频分量信号滤除近70%，有效提高数据处理精度。经标模试验测试验证，所提方法可成功应用于某些试验。
- 风洞 /
- 应变天平 /
- 连续变迎角 /
- 极小方差 /
- 滤波 /
- 低频振动
Abstract:
When the traditional step sweeping attack angle data processing method is used for directly processing the test data of continuous sweeping attack angle test, the low-frequency vibration component cannot be removed. Based on the characteristics of approximate symmetry and local linearity in the test data, a piecewise binomial fitting method is presented. Principle of the method is presented, and its minimum variance based piecewise length determination is introduced in details. The experimental results based on the test signal show that the method can filter the low-frequency component signal by nearly 70%, effectively improving the data processing accuracy. The method is also verified by common mode test, and has been successfully applied to some model tests.
- wind tunnel /
- strain balance /
- continuous sweeping angle of attack /
- minimum variance /
- filter /
- low-frequency vibration

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图 1 连续变迎角试验数据及其功率谱

Figure 1. Data of continuous sweeping angle of attack test and its power spectrum

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图 2 振动数据误差特性

Figure 2. Error characteristics of vibration data

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图 3 方法实现步骤

Figure 3. Implementation steps of proposed method

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图 4 某试验数据及拟合方差

Figure 4. Data of a test and its fitting variance

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图 5 数据分段

Figure 5. Data segmentation

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图 6 长度搜索过程中的方差变化

Figure 6. Variance change during length search

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图 7 分段拟合结果

Figure 7. Results of piecewise fitting

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图 8 滤波结果及对应残差

Figure 8. Filtered results and corresponding residuals

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图 9 测试信号

Figure 9. Test signal

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图 10 测试信号滤波结果及对应残差

Figure 10. Filtered results and corresponding residuals of test signal

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图 11 测试信号及滤波后的功率谱

Figure 11. Power spectrum of test signals and filtered results

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图 12 某高超标模测试结果

Figure 12. Test results of a hypervelocity normal mode

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图 13 某试验模型测试结果

Figure 13. Test results based on some test model

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表 1 试验数据分析结果

Table 1. Analyzed results of test data

y	power_k/%	power_p_k/%	max_derta_k/%	max_derta_p_k/%	derta_k/10^-6	derta_p_k/10^-6
y₁	0.104 4	0.115 2	0.222 3	0.245 9	-10.07	-11.11
y₂	0.035 7	0.039 4	0.083 3	0.092 0	7.91	8.73
	34.24	34.24	37.40	37.40	-78.52	-78.52

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表 2 滤波效果分析

Table 2. Analyzed filtering results

α/(°)	阶梯数据U₀/V	滤波前数据U₁/V	滤波后数据U₂/V	滤波前偏差e₁/%	滤波后偏差e₂/%
-6.45	0.959 8	0.955 8	0.955 2	-0.41	-0.47
-4.30	0.911 6	0.912 4	0.908 7	0.09	-0.32
-3.24	0.878 7	0.880 4	0.881 1	0.19	0.28
-2.20	0.832 8	0.829 9	0.833 8	-0.35	0.12
-1.79	0.803 1	0.807 2	0.805 8	0.52	0.33
-1.16	0.762 7	0.767 1	0.769 2	0.58	0.85
-0.13	0.734 0	0.735 9	0.735 3	0.27	0.19
0.28	0.758 8	0.756 9	0.761 3	-0.25	0.33
0.91	0.805 4	0.809 5	0.804 7	0.52	-0.08
1.97	0.805 2	0.812 6	0.812 1	0.93	0.86
3.02	0.809 4	0.809 3	0.812 8	-0.01	0.41
4.08	0.851 6	0.851 9	0.851 6	0.04	0
6.20	0.904 2	0.902 9	0.902 8	-0.13	-0.14

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