Development of an atmosphere temperature measurement system based on computational fluid dynamics and neural network algorithms

Abstract

To study the impact of atmospheric temperature on architectural design, energy-saving control systems, and smart home systems, the accuracy of atmospheric temperature measurement systems must be increased by 0.1 °C or higher. However, given that existing temperature measurement systems have difficulty eliminating radiation interference, they may have a radiation error of approximately 1 °C. Therefore, we designed a novel temperature measurement system comprising three sensor probes and a radiation shield. The thin film of the platinum resistance probe was fixed at the center of a spherical copper shell. These plates can prevent all types of radiation from reaching the probe surface. The lower plate could effectively guide the airflow to the sensor probes. The radiation error can be decreased by reducing the radiation interference and increasing the air velocity. The system radiation errors under different environmental conditions were quantified using a computational fluid dynamics (CFD) approach. A neural network algorithm was used to fit the CFD simulation data to form a radiation error correction method with high accuracy and universality. Experiments conducted under different environments revealed that the average radiation error of the system was +0.09 °C. The root mean square error and mean absolute error between the experimental radiation errors and the radiation errors provided by the correction method were +0.037 °C and +0.031 °C, respectively. The correlation coefficient between the temperature of the new system and the reference temperature was 0.999. Our findings suggest that this new system may potentially reduce the radiation error to within 0.1 °C.