基于CFD的地表温度传感器的设计与实验研究

Design and trial of surface temperature sensor based on CFD

地面温度变化速度为每10年升高0.1℃,然而受太阳辐射的影响,在实际测量时传统防辐射罩会产生约1 K量级的太阳辐射误差.为提升地表温度测量精度,降低工作能耗,本文设计了一种基于压电振片振动加速传感器探头辐射热量扩散的温度传感器.首先利用计算流体动力学(Computational Fluid Dynamics, CFD)方法计算温度传感器在多物理因素下的辐射误差,然后使用神经网络算法对数据进行拟合分析,最后搭建外场实验平台,将传感器放置在真实环境中来验证方案的可行性.实验结果表明,本文提出的地表温度传感器具有较高的温度测量精度,其修正值与基准值的绝对误差和均方根误差分别为0.041℃和0.055℃,也验证了神经网络算法修正效果的优越性.

The ground temperature changes at a rate of 0.1℃every 10 years,however,a solar radiation error of about 1 K can be produced by conventional radiation shields due to the influence of solar radiation.In order to improve the accuracy of surface temperature measurement and reduce working energy consumption,this paper designs a temperature sensor based on the accelerated diffusion of radiant heat via piezoelectric ceramic vibration.First,the Computational Fluid Dynamics(CFD)method is used to calculate the radiation error of the temperature sensor under multi physical factors,then the data is fitted and analyzed using the neural network algorithm,and finally the field experiment platform is built to place the temperature sensor in real environment to verify the feasibility of the scheme.The experimental results show that the absolute error and root mean square error between the corrected value and reference value of the surface temperature sensor are 0.041℃and 0.055℃,respectively,which also verifies the superior correction performance of the neural network algorithm.