Influence of Atmosphere on Calibration of Radiation Thermometers

Abstract

Current process of calibrating radiation thermometers, including thermal imagers, relies on measurement comparison with the temperature of a black body at a set distance. Over time, errors have been detected in calibrations of some radiation thermometers, which were correlated with moisture levels. In this study, effects of atmospheric air on thermal transmission were evaluated by the means of simulations using best available resources of the corresponding datasets. Sources of spectral transmissivity of air were listed, and transmissivity data was obtained from the HITRAN molecular absorption database. Transmissivity data of molecular species was compiled for usual atmospheric composition, including naturally occurring isotopologs. Final influence of spectral transmissivity was evaluated for spectral sensitivities of radiation thermometers in use, and total transmissivity and expected errors were presented for variable humidity and measured temperature. Results reveal that spectral range of measurements greatly influences susceptibility of instruments to atmospheric interference. In particular, great influence on measurements is evident for the high-temperature radiation pyrometer in the spectral range of 2–2.7 µm, which is in use in our laboratory as a traceable reference for high-temperature calibrations. Regarding the calibration process, a requirement arose for matching the humidity parameters during the temperature reference transfer to the lower tiers in the chain of traceability. Narrowing of the permitted range of humidity during the calibration, monitoring, and listing of atmospheric parameters in calibration certificates is necessary, for at least this thermometer and possibly for other thermometers as well.

Traceable Spectral Radiation Model of Radiation Thermometry

“…Due to Kirchhoff's law (4), the spectral absorptivity is equal to the spectral emissivity, therefore the absorbed radiation is replaced by the proportional part of the atmospheric self-emission. The spectral transmissivity of the atmosphere is relatively high under the usual indoor conditions, as already determined by the authors for the purpose of this study [9]. The spectral transmissivity of the air is shown in Figure 2.…”

“…The spectral emissivity can be converted into an effective scalar emissivity utilizing the expression for the measured spectral radiance of the emission 𝑀𝑀 𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 in equation (9),…”

Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

“…Similarly, the process followed for data acquisition in sensor-based measurement significantly affects the accuracy. A set of proportional and integral controllers are implemented in (Mlejnek & Ripka, 2016) to measure the output of the Anisotropic Magneto Resistive (AMR) current sensor to eliminate the inaccuracy related to nonlinearity. Similarly, two sets of auto-adjustable Successive Approximation Resistors (SARs) based on control logic are implemented to auto-adjust the precision, as discussed in (Fan et al, 2018).…”

“…Several works of literature report the undesired nonlinearity due to many environmental and operating environments. For example, the measurement accuracy of a thermal sensor in (Mlacnik & Pušnik, 2021) behaves nonlinearly due to the impact of moisture and carbon dioxide gas in the atmosphere where measuring equipment is placed. The presence of frequency components in the input signal affects the linear response of impedance measurement, as discussed in (Santoni et al, 2021).…”

“…However, these methods are unsuitable for calibrating online and multimodal systems with nonlinear responses. The multivariate calibration methods discussed in (Mlacnik & Pušnik, 2021; Sabahno et al, 2020) suffer from the boundary and extra information aquisition problems. The calibration of the battery management system with a digital Finite Impulse Response filter in (Santoni et al, 2021) introduces harmonic distortions and makes it an inefficient choice for nonlinear impedance calibration.…”