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Article Abstract
To minimize the impact of various radiations on atmospheric temperature observation, a new natural ventilation temperature observation instrument is designed in this paper. First of all, the temperature measuring instrument model is constructed using the means of computational fluid dynamics. Then, the radiation error of the device is quantified in different environmental conditions. Next, a back propagation neural network algorithm is adopted to fit a radiation error modified equation with multivariable changes. Finally, the measured values of a 076B forced ventilation temperature monitoring device are adopted as the temperature reference, and field tests are conducted. The average error of this new device is 0.12 °C. The root mean square error, mean square error, and correlation coefficient between the measured values of the new instrument and the reference temperature are 0.047 °C, 0.036 °C, and 0.999 °C, respectively.
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