Design and application of a high-precision counterweighted self-calibrating surface thermometer.

In this study, a high-precision counterweight self-calibrating surface thermometer is designed to reduce human and environmental influences on a thermocouple surface thermometer during measuring. A self-weighted spring structure based on a copper substrate is designed to ensure perfect contact between the surface thermometer and the temperature source. In conjunction, a wind guard is coupled with insulating materials to optimize the thermal exchange of the surface thermometer. Subsequently, the maximum error is reduced to ±1.5 °C by system hardware optimization. However, hardware calibration alone is insufficient. Furthermore, a back propagation neural network is employed to calibrate the surface thermometer. Temperature sensor data are collected under various surface source temperatures and airflow velocities to train the neural network. Hence, the effectiveness of the proposed Gaussian function in enhancing the measurement accuracy of the surface temperature sensor is demonstrated. The results show higher stability and repeatability in temperature measurement than thermocouple-based surface thermometers. The proposed thermometer exhibits robustness against environmental and operational variability with a maximum indication error of -0.2 °C. In contrast, the maximum error of the surface thermometer is between -2.8 and -6.8 °C. Regarding repeatability, the standard deviation with the proposed device is 0.2%, highlighting its accuracy and consistency of performance. These results can mostly be attributed to the synergistic effect of clever mechanical design and software optimization, resulting in a surface thermometer with outstanding accuracy and repeatability.