基于CFD和BP神经网络的超声测风仪阴影效应补偿研究
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摘要
风速风向是气象观测的关键性参数,阴影效应对超声测风仪测量风速风向有显著影响。为了消除不同风速风向和温度条件下阴影效应导致的测量误差,结合超声波时差法测风原理提出了一种基于计算流体力学(CFD)仿真和反向传播(BP)神经网络数据处理的阴影效应补偿方法。首先通过CFD仿真软件Fluent对超声测风仪的风场进行模拟分析,获取了受阴影效应影响的风速、风向以及温度样本数据,然后利用这些样本从减小相对误差方面对BP神经网络算法在超声测风仪中应用的性能进行了研究与分析,最后利用超声测风仪风洞实验的测量数据对本文的阴影效应补偿方法的准确性进行了验证。研究结果表明:通过基于CFD和BP神经网络的预测模型对阴影效应进行修正后,测试系统风速风向的测量精度有显著的提升,可满足高精度超声测风的测量需求。本文的研究结果对于高精度超声测风仪的研制有一定的参考价值。
Wind is a critical parameter of meteorological observation,and the shadow effect has a significant effect on the wind speed and direction measured by the ultrasonic anemometer. In order to eliminate the measurement error caused by the shadow effect under different wind speed and temperature conditions,a shadow effect compensation method based on computational fluid dynamics(CFD) simulation and back propagation(BP) neural network data processing is proposed in combination with the measuring principle of the ultrasonic time difference method. Firstly,the CFD simulation software—Fluent is used to simulate the wind field of ultrasonic anemometer. The wind speed,wind direction and temperature sample data affected by the shadow effect are obtained,and then the performance of BP neural network algorithm in ultrasonic anemometer is studied and analyzed by using these samples to reduce the relative error. Finally,the accuracy of the shadow effect compensation method in this paper is validated by using the wind tunnel data of the ultrasonic anemometer. The results show that the accuracy of the wind measurement is significantly improved after correcting the shadow effect by the prediciton model based on CFD and BP neural network,which can meet the high precision measurement requirements of ultrasonic anemometer. The results of this study have a certain reference value for the development of high precision ultrasonic wind measurement instrument.
Wind is a critical parameter of meteorological observation,and the shadow effect has a significant effect on the wind speed and direction measured by the ultrasonic anemometer. In order to eliminate the measurement error caused by the shadow effect under different wind speed and temperature conditions,a shadow effect compensation method based on computational fluid dynamics(CFD) simulation and back propagation(BP) neural network data processing is proposed in combination with the measuring principle of the ultrasonic time difference method. Firstly,the CFD simulation software—Fluent is used to simulate the wind field of ultrasonic anemometer. The wind speed,wind direction and temperature sample data affected by the shadow effect are obtained,and then the performance of BP neural network algorithm in ultrasonic anemometer is studied and analyzed by using these samples to reduce the relative error. Finally,the accuracy of the shadow effect compensation method in this paper is validated by using the wind tunnel data of the ultrasonic anemometer. The results show that the accuracy of the wind measurement is significantly improved after correcting the shadow effect by the prediciton model based on CFD and BP neural network,which can meet the high precision measurement requirements of ultrasonic anemometer. The results of this study have a certain reference value for the development of high precision ultrasonic wind measurement instrument.
引文
[1]Han D,Park S.Measurement Range Expansion of Congtinuous Wave Ultrasonic Anenmometer[J].Measurement,2011,44(10):1909-1914.
[2]王国峰,赵永生,范云生.风速风向测量误差补偿算法的研究[J].仪器仪表学报,2013,34(4):786-790.
[3]李聪,国红玉,王峰.二维超声波风速仪[J].仪表技术与传感器,2015,(2):29-32+53.
[4]Grare L,Lenain L,Melville W K.The Influence of Wind Direction on Campbell Scientific CSAT3 and Gill R3-50 Sonic Anemometer Measurements[J].Journal of Atmospheric and Oceanic Technology,2016,33(11):2477-2497.
[5]Lopes G M G,Da Silva Junior D P,De Fran9a J A,et al.Development of 3-D Ultrasonic Anemometer with Nonorthogonal Geometry for the Determination of High-Intensity Winds[J].IEEE Transactions on Instrumentation and Measurement,2017,PP(99):1-9.
[6]Wyngaard J C,Zhang S F.Transducer-Shadow Effects on Turbulence Spectra Measured by Sonic Anemometers[J].Journal of Atmospheric and Oceanic Technology,1985,2(12):548-558.
[7]黄敏,卢会国,王保强,等.超声测风传感器在回路风洞中的测试[J].气象科技,2016,44(1):14-18.
[8]Kaimal J C.Sonic Anemometer Measurement of Atmospheric Turbulence[C]//Proceedings of the Dynamic Flow Conference 1978on Dynamic Measurements in Unsteady Flows,Springer,Dordrecht,1978:551-565.
[9]Horst T W,Semmer S R,Maclean G.Correction of a Non-Orthogonal Three-Component Sonic Anemometer for Flow Distortion by Transducer Shadowing[J].Boundary-Layer Meterorol,2015,155(2):371-395.
[10]Frank J M,Massman A W J,Swiatek E,et al.All Sonic Anemometers Need to Correct for Transducer and Structural Shadowing in Their Velocity Measurements[J].Journal of Atmospheric and Oceanic Technology,2016,33(1):149-167.
[11]沙弈卓,畅世聪,高庆亭,等.用于超声风速仪标定的低速气象风洞设计[J].仪器仪表学报,2008,29(8):296-300.
[12]行鸿彦,于祥,邹水平,等.风杯式风速传感器启动风速校准实验箱的分析与设计[J].仪器仪表学报,2015,36(9):1996-2004.
[13]冒晓莉,张加宏,肖韶荣,等.基于流体动力学的探空仪GTS1湿度测量误差修正研究[J].地球物理学报,2016,59(12):4791-4805.
[14]Yang J,Liu Q,Dai W.Computational Fluid Dynamics Analysis and Experimental Study of a Low Measurement Error Temperature Sensor Used in Climate Observation[J].Review of Scientific Instruments,2017,88(2):024902.
[15]张加宏,刘毅,顾芳,等.基于PSO-BP神经网络的气溶胶质量浓度测量系统湿度补偿[J].传感技术学报,2017,30(3):360-367.
[16]Socha K,Socha M.Hot-Wire Anemometric Method for Flow Velocity Vector Measurement in 2D Gas Flows Based on Artificial Neural Network[J].Flow Measurement and Instrumentation,2015,46:163-169.
[17]Zhang J H,Wu Y S,Liu Q,et al.Research on High-Precision,Low Cost Piezoresitive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements[J].Micomachines,2015,6:554-573.
[18]帅师师,王露,方鑫,等.基于超声波风速风向测速算法研究[J].压电与声光,2015,37(3):468-472.
[19]杨世铭,陶文铨.传热学[M].北京:高等教育出版社,2011.
[20]黄衍标,罗广岳,何铭金.BP神经网络在巡逻机器人多传感器数据融合中的应用[J].传感技术学报,2016,29(12):1936-1940.
[21]Kit E,Cherkassky A,Sant T,et al.In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer:Implementation of a Neural Network[J].Journal of Atmospheric and Oceanic Technology,2010,27(1):23-41.
[22]行鸿彦,邹水平,徐伟,等.基于PSO-BP神经网络的湿度传感器温度补偿[J].传感技术学报,2015,28(6):864-869.
[2]王国峰,赵永生,范云生.风速风向测量误差补偿算法的研究[J].仪器仪表学报,2013,34(4):786-790.
[3]李聪,国红玉,王峰.二维超声波风速仪[J].仪表技术与传感器,2015,(2):29-32+53.
[4]Grare L,Lenain L,Melville W K.The Influence of Wind Direction on Campbell Scientific CSAT3 and Gill R3-50 Sonic Anemometer Measurements[J].Journal of Atmospheric and Oceanic Technology,2016,33(11):2477-2497.
[5]Lopes G M G,Da Silva Junior D P,De Fran9a J A,et al.Development of 3-D Ultrasonic Anemometer with Nonorthogonal Geometry for the Determination of High-Intensity Winds[J].IEEE Transactions on Instrumentation and Measurement,2017,PP(99):1-9.
[6]Wyngaard J C,Zhang S F.Transducer-Shadow Effects on Turbulence Spectra Measured by Sonic Anemometers[J].Journal of Atmospheric and Oceanic Technology,1985,2(12):548-558.
[7]黄敏,卢会国,王保强,等.超声测风传感器在回路风洞中的测试[J].气象科技,2016,44(1):14-18.
[8]Kaimal J C.Sonic Anemometer Measurement of Atmospheric Turbulence[C]//Proceedings of the Dynamic Flow Conference 1978on Dynamic Measurements in Unsteady Flows,Springer,Dordrecht,1978:551-565.
[9]Horst T W,Semmer S R,Maclean G.Correction of a Non-Orthogonal Three-Component Sonic Anemometer for Flow Distortion by Transducer Shadowing[J].Boundary-Layer Meterorol,2015,155(2):371-395.
[10]Frank J M,Massman A W J,Swiatek E,et al.All Sonic Anemometers Need to Correct for Transducer and Structural Shadowing in Their Velocity Measurements[J].Journal of Atmospheric and Oceanic Technology,2016,33(1):149-167.
[11]沙弈卓,畅世聪,高庆亭,等.用于超声风速仪标定的低速气象风洞设计[J].仪器仪表学报,2008,29(8):296-300.
[12]行鸿彦,于祥,邹水平,等.风杯式风速传感器启动风速校准实验箱的分析与设计[J].仪器仪表学报,2015,36(9):1996-2004.
[13]冒晓莉,张加宏,肖韶荣,等.基于流体动力学的探空仪GTS1湿度测量误差修正研究[J].地球物理学报,2016,59(12):4791-4805.
[14]Yang J,Liu Q,Dai W.Computational Fluid Dynamics Analysis and Experimental Study of a Low Measurement Error Temperature Sensor Used in Climate Observation[J].Review of Scientific Instruments,2017,88(2):024902.
[15]张加宏,刘毅,顾芳,等.基于PSO-BP神经网络的气溶胶质量浓度测量系统湿度补偿[J].传感技术学报,2017,30(3):360-367.
[16]Socha K,Socha M.Hot-Wire Anemometric Method for Flow Velocity Vector Measurement in 2D Gas Flows Based on Artificial Neural Network[J].Flow Measurement and Instrumentation,2015,46:163-169.
[17]Zhang J H,Wu Y S,Liu Q,et al.Research on High-Precision,Low Cost Piezoresitive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements[J].Micomachines,2015,6:554-573.
[18]帅师师,王露,方鑫,等.基于超声波风速风向测速算法研究[J].压电与声光,2015,37(3):468-472.
[19]杨世铭,陶文铨.传热学[M].北京:高等教育出版社,2011.
[20]黄衍标,罗广岳,何铭金.BP神经网络在巡逻机器人多传感器数据融合中的应用[J].传感技术学报,2016,29(12):1936-1940.
[21]Kit E,Cherkassky A,Sant T,et al.In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer:Implementation of a Neural Network[J].Journal of Atmospheric and Oceanic Technology,2010,27(1):23-41.
[22]行鸿彦,邹水平,徐伟,等.基于PSO-BP神经网络的湿度传感器温度补偿[J].传感技术学报,2015,28(6):864-869.