Data processing method for multi-spectral radiometric thermometry based on IDPHGA

Abstract

Oxidized high-temperature alloys are known for their excellent thermal properties in high-temperature environments, making them suitable for complex working conditions such as gas turbines and aero-engines. The temperature of oxidized alloys is measured to provide a more comprehensive reflection of their performance under actual working conditions, thereby offering important data support for engineering design and safety assessment. However, the accurate measurement of surface temperature when spectral emissivity is unknown remains a pressing challenge. At high temperatures or under extreme conditions, further changes may occur in the oxide layer, such as exfoliation and re-oxidation, which can affect its emissivity. Conventional methods that assume a emissivity model are unable to accurately accommodate these alterations. In response, an Improved Double-Population Hybrid Genetic Algorithm (IDPHGA) for multi-spectral radiometric thermometry is proposed in this paper, enabling the simultaneous estimation of temperature and emissivity of the oxidized samples without the need to assume an emissivity model. The temperature testing of oxidized GH3128, GH4037, GH4169, and GH536 samples across various temperature points was conducted utilizing the IDPHGA method, the average absolute temperature measurement errors obtained were not greater than 4.7 K, 4.7 K, 5.4 K, and 4.6 K, respectively. The experimental outcomes have verified that the method proposed in this paper possesses superior accuracy and robustness in the measurement of real objects. The validation is significant for promoting the extensive application of high-temperature alloy materials in the fields of aviation, navigation, and other high-temperature applications.