摘要
太阳辐射是影响大气温度观测精度的主要因素,为降低辐射误差,文中提出一种阵列式球形温度传感器设计,无需安装于百叶箱或防辐射罩内。该传感器利用阵列式球形温度传感器之间的辐射误差比值关系,可推导出环境温度的真实值。首先,利用计算流体动力学(CFD)方法计算出阵列式球形温度传感器相互之间的辐射误差比值。然后,采用极限学习机(ELM)算法获得该比值与环境各因素之间的关系。最后,为验证阵列式球形温度传感器的观测精度,以基于太阳模拟器的实验平台测试数据为参考,进行标准气温测量对比实验。实验结果表明,文中提出的阵列式球形温度传感器均方根误差为0.16 K。
Solar radiation is the main factor affecting the accuracy of atmospheric temperature observation.In order to reduce the radiation error,this paper proposed an array type spherical temperature sensor design,which did not need to be installed in a louver or radiation shield.The sensor used the radiation error ratio relationship between the array type spherical temperature sensors to deduce the true value of the ambient temperature.First,the radiation error ratio was calculated between the array spherical temperature sensors using computational fluid dynamics(CFD)method.Then,the extreme learning machine(ELM)algorithm was used to obtain the relationship between the ratio and various environmental factors.Finally,in order to verify the observation accuracy of the array-type spherical temperature sensor,a standard temperature measurement comparison experiment was carried out based on the test data of the experimental platform based on the solar simulator.Experimental results show that the root mean square error of the array spherical temperature sensor proposed in this paper is 0.16 K.
作者
丁枫
刘清惓
杨杰
陈高颖
袁宇
付川琪
DING Feng;LIU Qing-quan;YANG Jie;CHEN Gao-ying;YUAN Yu;FU Chuan-qi(Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science and Technology,Nanjing 210044,China;Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Meteorological Sensor Network Technology Engineering Center,Nanjing 210044,China)
出处
《仪表技术与传感器》
CSCD
北大核心
2021年第7期16-20,共5页
Instrument Technique and Sensor
关键词
太阳辐射
温度传感器
辐射误差比值
计算流体动力学
极限学习机
太阳模拟器
solar radiation
temperature sensor
radiation error ratio
computational fluid dynamics
extreme learning machine
solar simulator
