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Multispectral True Temperature Inversion Based on Multi-Objective Minimum Optimization Principle of Reference Temperature (IMR) |
ZHANG Fu-cai1, 2, LIU Yun-gang1, SUN Xiao-gang2* |
1. School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
2. School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China |
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Abstract Multi-spectral radiation thermometry is a non-contact temperature measurement method which can retrieve the true temperature of the radiator. The method collects the brightness and temperature information of the target under different wavelengths and retrieves the true temperature of the target using related algorithms. Multi-spectral pyrometer is one of the most important measuring tools to retrieve the true temperature of the target by this method. After nearly half a century of unremitting efforts and exploration, many scholars at home and abroad have made considerable progress. Because the spectral emissivity is less than 1, the true temperature of the target can not be measured directly by using a radiation pyrometer. Only by processing the wavelength and brightness temperatures of multiple spectral channels and using the processing technology of multi-spectral radiation temperature measurement data can the true temperature of the target be obtained. In the process of true temperature inversion, it is generally necessary to find the functional relationship between spectral emissivity and wavelength or temperature variables, and replace spectral emissivity with expressions containing wavelength or temperature variables. This method lacks sufficient theoretical support for model selection. For non-professionals, it is difficult to select a suitable spectral emissivity model. The solution to the equation is realized. Because of the instantaneous variability of spectral emissivity, there are always some differences between the assumed spectral emissivity model and the actual spectral emissivity, which may lead to large errors in a true temperature inversion. In addition, the mathematical model between spectral emissivity and wavelength or temperature variables needs a lot of experiments and experience to determine. This mathematical model has poor generality, especially when the radiator to be measured changes; this mathematical model loses its significance. Therefore, it is an urgent need to find a universal multi-spectral true temperature inversion method without assuming a mathematical model between spectral emissivity and wavelength or temperature. For each spectral channel of a multi-wavelength pyrometer, the measured data of each spectral channel satisfies a mathematical equation, and for each spectral channel, an undetermined system of equations can be formed. In order to solve this system of equations, the idea of optimization is introduced into the process of multi-spectral solution. A multi-spectral true temperature inversion method based on multi-objective minimum optimization principle of reference temperature is proposed. The problem of solving multi-spectral true temperature is transformed into a multi-objective extreme value optimization problem, and the inversion of true temperature and spectral emissivity without assuming the spectral emissivity model is realized. Compared with the traditional quadratic measurement method, the new method has the same inversion accuracy as the quadratic measurement method, but inversion speed has been greatly improved. With the help of the true measurement data measured by previous scholars, the inversion of true temperature and spectral emissivity is realized by using the multi-spectral true temperature inversion method based on the multi-objective minimum optimization principle of reference temperature. The new method has greatly improved the inversion speed. With the help of the true measurement data of plume temperature of solid rocket motor in the past, the inversion of true temperature is realized by using a multi-objective minimum optimization method based on reference temperature.
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Received: 2019-03-01
Accepted: 2019-07-14
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Corresponding Authors:
SUN Xiao-gang
E-mail: qingtengzfc@yeah.net
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[1] Araújo A. Infrared Physics & Technology, 2016, 76: 365.
[2] Svet D Y. High Temperatures High Pressures, 1976, 8(5): 493.
[3] Rodiet C, Rémy B, Degiovanni A, et al. Quantitative InfraRed Thermography Journal, 2013, 10(2): 222.
[4] SHAO Yan-ming, ZHAO Shu-an, CHEN Yan-ru, et al(邵艳明, 赵书安, 陈延如,等). Acta Optica Sinica(光学学报), 2015, 35(11): 315.
[5] Zhang L, Dai J M, Yin Z. Chinese Optics Letters, 2015, 13(6): 83.
[6] YANG Yi-fan, CAI Hong-xing, WANG Zhao-xuan, et al(杨艺帆, 蔡红星, 王诏宣, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(3):702.
[7] SUN Xiao-gang, HE Jin, DAI Jing-min(孙晓刚, 何 瑾, 戴景民, 等). Journal of Harbin Institute of Technology(哈尔滨工业大学学报), 1998, 30(6): 1.
[8] CONG Da-cheng, DAI Jing-min, SUN Xiao-gang, et al(丛大成, 戴景民, 孙晓刚, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2001, 20(2): 97.
[9] Song Y, Sun X, Tang H. Chinese Optics Letters, 2007, 5(8): 457.
[10] Mei Liang, Bojun Sun, Xiaogang Sun, et al. International Journal of Thermophysics, 2017, 38(3): 35.
[11] SUN Xiao-gang, DAI Jing-min, CONG Da-cheng, et al(孙晓刚, 戴景民, 丛大成, 等). Journal of Tsinghua University·Science and Technology(清华大学学报·自然科学版), 2003, 43(7): 916.
[12] SUN Xiao-gang, DAI Jing-min, WANG Xue-feng, et al(孙晓刚, 戴景民, 王雪峰, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2003, 22(2): 141. |
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