Due to its dynamic and non-invasive characteristics, it is difficult to obtain stable and accurate measurement results using contact thermocouple temperature measurement. The temperature measurement accuracy of infrared thermal imaging cameras in non-invasive temperature measurement is affected by factors such as flame emissivity and radiation path attenuation, and there is also a large temperature measurement error. Among them, the colorimetric thermometer can avoid the influence of the emissivity of the measured target, and has the advantages of high accuracy, strong anti-interference ability, and wide temperature measurement range. However, in engineering use, due to the energy zero points set by long-wave and short-wave radiation, the data calculated and output based on the principle of colorimetric thermometry has a "temperature breakpoint" phenomenon. Based on this kind of engineering situation, this paper proposes a temperature calibration method for colorimetric thermometers and a temperature data breakpoint repair method. The actual measurement and verification experiment of a high-temperature blackbody furnace has proven the effectiveness of this temperature data breakpoint repair method, providing more accurate temperature data values for experiments such as flame temperature testing.
KEYWORDS: Temperature metrology, Calibration, Education and training, Black bodies, Neural networks, Pyrometry, Temperature distribution, Sensors, Infrared radiation, Quantum processes
Explosive fireballs generate high temperature, high pressure, and strong shock waves during the combustion process, and accurate measurement of their temperature is crucial in engineering applications. Multi spectral radiation temperature measurement technology, as an efficient non-contact detection method, has significant advantages. This study conducted temperature calibration experiments on a multispectral temperature measurement system using a 25 channel multispectral camera and a standard medium temperature blackbody furnace, and processed the calibrated grayscale images using a fully connected neural network. Generate a high-precision temperature distribution map by inputting grayscale images at 25 wavelengths into the network. This method not only improves the accuracy of temperature measurement, but also lays the experimental foundation for subsequent measurement of explosion temperature field. The research results provide key technical support for temperature measurement in engineering applications and are expected to play an important role in related fields.
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