基于BLDC的气象温度传感器设计与实验研究

Design and experimental research of meteorological temperature sensor based on BLDC

针对自然通风温度传感器受太阳辐射影响后测量精度较差,而强制通风温度传感器功耗大、能耗浪费严重等问题,设计了一种基于无刷直流电机(BLDC),将自然通风与强制通风相结合的新型温度传感器。首先利用计算流体动力学(CFD)方法量化传感器在不同环境变量及电机转速下的辐射误差,并基于此制定电机转速的控制策略。其次利用粒子群优化的神经网络算法对仿真数据进行训练学习,并拟合成辐射误差修正方程。最后搭建户外测试平台验证新型温度传感器的测温精度以及修正方程的修正效果。实验结果表明,新型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差值的平均绝对误差(MAE)和均方根误差(RMSE)分别为0.035℃和0.043℃。

To address such problems as inaccurate measurement of natural ventilation temperature sensor under solar radiation,high power consumption and serious waste of energy consumption of forced ventilation temperature sensor,a new type of temperature sensor was designed based on Brushless Direct Current Motor(BLDC),which combined natural and forced ventilation.Firstly,computational fluid dynamics(CFD)method was used to quantify the radiation error of the sensor under different environmental variables and motor speeds.Control strategy of motor speed was then developed based on above results.Secondly,particle swarm optimization neural network algorithm was used to train and learn the simulation data,and the radiation error correction equation was synthesized.Finally,an outdoor test platform was built to verify the accuracy of the new temperature sensor and the correction effect of the modified equation.The experimental results show that the new temperature sensor has high temperature measurement accuracy,and the mean absolute error(MAE)and root mean square error(RMSE)of the difference between the modified value and the reference value are 0.035℃and 0.043℃,respectively.