三维流场测量中的微风量多传感器数据融合

doi:10.16180/j.cnki.issn1007-7820.2021.12.014

摘要/Abstract

摘要：

封闭三维空间的流场分布对通风系统设计至关重要。文中针对多个传感器组成的风速传感器阵列的测量数据处理,提出一种基于相关性函数-卡尔曼滤波算法的多传感器数据融合算法。对多个传感器测量的无效数据点,运用相关性判定方法,排除测量数据中的无效数据。然后将传感器标定输出数据和方差作为卡尔曼滤波的初始预估值和初始方差估计,进行多传感器数据的融合。与普通传感器标定的测量相比,通过该方法融合得到的风速数据测量误差更小。测量数据融合实验结果表明,该方法能有效提高测量精度,基于该数据处理方法得到的三维空间流场测量实验结果准确可靠。

关键词: 风速, 通风, 流场, 测量, 传感器, 标定, 滤波, 数据融合

Abstract:

Flow field in the enclosed three-dimensional space is very important for the design of the ventilation system. In view of the data processing of the air velocity sensor array composed of multiple sensors, a multi-sensor data fusion algorithm based on the correlation function-Kalman filter algorithm is proposed in this study. Invalid data acquired by flow sensors is excluded by correlation judgement in measuring. Then, the sensor calibration output data and variance are used as the initial estimated value and variance estimation of Kalman filter to perform the multi-sensor data fusion. Compared with the measurement of common sensor calibration, the measurement error of air velocity obtained by this method is smaller. The experimental results show that the method can effectively improve the measurement accuracy, and the experimental results of the three-dimensional flow field measurement based on the data processing method are accurate and reliable.

Key words: air velocity, ventilation, flow field, measurement, sensor, calibration, filter, data fusion

中图分类号:

TP393

肖欣招,刘建旭,伍国靖,付东翔. 三维流场测量中的微风量多传感器数据融合[J]. 电子科技, 2021, 34(12): 81-86.

XIAO Xinzhao,LIU Jianxu,WU Guojing,FU Dongxiang. Multi-Sensor Data Fusion in Measurement of Flow Field of Air Velocity in Three-Dimensional Large Space[J]. Electronic Science and Technology, 2021, 34(12): 81-86.

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参考文献 18

[1]	杨福胜, 张早校, 王斯民, 等. 粒子追踪测速(PTV)技术及其在多相流测试中的应用[J]. 流体机械, 2014, 42(2):37-42.
	Yang Fusheng, Zhang Zaoxiao, Wang Simin, et al. Particle tracking velocimetry and its application to the measurement of multiphase flow-a review[J]. Fluid Machinery, 2014, 42(2):37-42.
[2]	王成军, 江平, 辛欣, 等. 基于PIV技术对三级旋流杯燃烧室流场的测量[J]. 航空动力学报, 2015, 30(5):1032-1039.
	Wang Chengjun, Jiang Ping, Xin Xin, et al. Measurement of triple-stage swirler cup combustor flow field based on PIV technology[J]. Journal of Aerospace Power, 2015, 30(5):1032-1039.
[3]	Wu Y, Chen Z, Hu L, et al. Identification of safety gas for fusion demonstration reactor[J]. Nature Energy, 2016, 1(12):1-11.
[4]	栾昆鹏, 叶景峰, 王晟, 等. 基于粒子图像测速的XeF(C-A)气体激光器增益区流场测量[J]. 中国激光, 2019, 46(2):132-138.
	Luan Kunpeng, Ye Jingfeng, Wang Sheng, et al. Flow field measurements in gain zone of XeF(C-A)gas laser based on particle image velocimetry[J]. Chinese Journal of Lasers, 2019, 46(2):132-138.
[5]	Qu X J, Song Y, Jin Y, et al. 3D SAPIV particle field reconstruction method based on adaptive threshold[J]. Applied Optics, 2018, 57(7):1622-1633. doi: 10.1364/AO.57.001622
[6]	Cozzi F, Coghe A, Sharma R. Analysis of local entrainment rate in the initial region of isothermal free swirling jets by stereo PIV[J]. Experimental Thermal and Fluid Science, 2018, 94(1):281-294. doi: 10.1016/j.expthermflusci.2018.01.013
[7]	Im S, Kim H T, Rhee B W, et al. PIV measurements of the flow patterns in a CANDU-6 model[J]. Annals of Nuclear Energy, 2016, 98(2):1-11. doi: 10.1016/j.anucene.2016.07.012
[8]	Takeda Y. Velocity profile measurement by ultrasound Doppler shift method[J]. International Journal of Heat and Fluid Flow, 1986, 7(4):313-318. doi: 10.1016/0142-727X(86)90011-1
[9]	刘建书, 李人厚, 常宏. 基于相关性函数和最小二乘的多传感器数据融合[J]. 控制与决策, 2006, 21(6):714-720.
	Liu Jianshu, Li Renhou, Chang Hong. Multi-sensor data fusion based on correlation function and least square[J]. Control and Decision, 2016, 21(6):714-720.
[10]	刘春, 马颖. 改进卡尔曼滤波在北斗伪距定位中的研究[J]. 电子测量与仪器学报, 2016, 30(5):779-785.
	Liu Chun, Ma Ying. Research on improved Kalman filter in Beidou pseudo ranges positioning[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(5):779-785.
[11]	卢治功, 贺鹏, 职连杰, 等. 基于最小二乘法多项式拟合三角测量模型研究[J]. 应用光学, 2019, 40(5):853-858. doi: 10.5768/JAO201940.0503003
	Lu Zhigong, He Peng, Zhi Lianjie, et al. Laser triangulation measurement model based on least square[J]. Journal of Applied Optics, 2019, 40(5):853-858. doi: 10.5768/JAO201940.0503003
[12]	周旋, 师蔚. 低功耗高精度温度多路采集测温系统研究[J]. 电子科技, 2020, 33(8):46-52.
	Zhou Xuan, Shi Wei. Research on low power and high precision temperature multichannel acquisition technology[J]. Electronic Science and Technology, 2020, 33(8):46-52.
[13]	张毅, 张宝芬, 曹丽, 等. 自动检测技术及仪表控制系统. [M]. 2版.北京: 化学工业出版社, 2004.
	Zhang Yi, Zhang Baofen, Cao Li, et al. Automatic detection technology and instrument control system [M].2nd ed. Beijing: Chemical Industry Press, 2004.
[14]	费业泰. 误差理论与数据处理[M]. 6版.北京: 机械工业出版社, 2010.
	Fei Yetai. Error theory and data processing[M].6th ed. Beijing: Machinery Industry Press, 2010.
[15]	黎蓉. 卡尔曼滤波在组合导航数据处理中的应用[J]. 电子测量技术, 2017, 40(3):158-162.
	Li Rong. Study on the application of Kalman filtering in the integrated navigation[J]. Electronic Measurement Technology, 2017, 40(3):158-162.
[16]	乐燕芬, 罗红玉, 赵妍, 等. 基于位置指纹的室内移动目标定位系统[J]. 电子科技, 2018, 31(11):42-46.
	Le Yanfen, Luo Hongyu, Zhao Yan, et al. Indoor positioning system based on fingerprint with enhancing accuracy[J]. Electronic Science and Technology, 2018, 31(11):42-46.
[17]	崔永林, 席燕辉, 张小东. 基于自适应卡尔曼滤波残差分析的谐波检测[J]. 电力系统保护与控制, 2019, 47(24):92-100.
	Cui Yonglin, Xi Yanhui, Zhang Xiaodong. Detection of harmonic based on residual analysis using adaptive Kalman filter[J]. Power System Protection and Control, 2019, 47(24):92-100.
[18]	李伟, 刘伟嵬, 邓业林. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程, 2020, 31(3):321-327.
	Li Wei, Liu Weiwei, Deng Yelin. SOC eEstimation for lithium-ion batteries based on EKF[J]. China Mechanical Engneering, 2020, 31(3):321-327.