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.
关键词
无刷直流电机/强制通风/温度传感器/辐射误差/计算流体力学
Key words
brushless direct current motor/forced ventilation/temperature sensor/radiation error/computational fluid dynamics
维普
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