Multi-wavelength radiometric thermometry data processing algorithm based on the BFGS algorithm.

Multi-wavelength radiometric thermometry has a wide application prospect in many fields. However, due to unknown emissivity, the data processing algorithm remains a difficult problem. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is proposed to inverse true temperature and spectral emissivity without assuming the emissivity model. The BFGS algorithm can automatically identify the emissivity models of different trends. These simulation results show that given different initial emissivity has no significant influence on the inverse temperature and emissivity. Then, we select 0.5 as the initial emissivity and carry out the simulation experiments at 800 and 900 K, respectively. The maximum absolute error of temperature is less than 3.5 K and the computation time is less than 0.2 s. Thus, the algorithm has high precision and efficiency. Finally, the verification experiment indicates that the BFGS algorithm is effective and reliable. The proposed method can be applied to real-time temperature measurement in many industrial processes.

[1]  Yufang Liu,et al.  Study of normal spectral emissivity of copper during thermal oxidation at different temperatures and heating times , 2019, International Journal of Heat and Mass Transfer.

[2]  Bo Peng,et al.  Generalized inverse matrix-exterior penalty function (GIM-EPF) algorithm for data processing of multi-wavelength pyrometer (MWP). , 2018, Optics express.

[3]  Xin Guo,et al.  Directly data processing algorithm for multi-wavelength pyrometer (MWP). , 2017, Optics express.

[4]  E. Gumbrell,et al.  A high spatio-temporal resolution optical pyrometer at the ORION laser facility. , 2016, The Review of scientific instruments.

[5]  C. Feng,et al.  Application of multispectral radiation thermometry in temperature measurement of thermal barrier coated surfaces , 2016 .

[6]  Huai-Chun Zhou,et al.  Measurement of distributions of temperature and wavelength-dependent emissivity of a laminar diffusion flame using hyper-spectral imaging technique , 2016 .

[7]  Xiaogang Sun,et al.  A data processing algorithm for multi-wavelength pyrometry-which does not need to assume the emissivity model in advance , 2015 .

[8]  PRAVEEN KRISHNAN,et al.  An artificial neural network based fast radiative transfer model for simulating infrared sounder radiances , 2012, Journal of Earth System Science.

[9]  Chang-Da Wen,et al.  Investigation of steel emissivity behaviors: Examination of Multispectral Radiation Thermometry (MRT) emissivity models , 2010 .

[10]  Mariusz Kastek,et al.  Automatic compensation of emissivity in three-wavelength pyrometers , 2007 .

[11]  Yang Chunling,et al.  The measuring of spectral emissivity of object using chaotic optimal algorithm , 2005 .

[12]  J. Dai,et al.  Processing Method of Multi-Wavelength Pyrometer Data for Continuous Temperature Measurements , 2005 .

[13]  T. Piatkowski,et al.  Multispectral precise pyrometer for measurement of seawater surface temperature , 2004 .

[14]  P. Coppa,et al.  Development of a Special Multi-Wavelength Pyrometer for Temperature Distribution Measurements in Rocket Engines , 2002 .

[15]  Adam Mazikowski,et al.  Modeling of noncontact temperature measurement system using multiwavelength pyrometry , 2001, Optoelectronic and Electronic Sensors.

[16]  Gustave C. Fralick,et al.  Use of a multiwavelength pyrometer in several elevated temperature aerospace applications , 2001 .

[17]  Abraham Katzir,et al.  Four-band fiber-optic radiometry for determining the “true” temperature of gray bodies , 2000 .

[18]  Paolo Coppa,et al.  The transient regime of a multiwavelength pyrometer , 1993 .

[19]  G. R. Gathers.  Monte Carlo studies of multiwavelength pyrometry using linearized equations , 1992 .

[20]  Xiaogang Sun,et al.  Development of a new fiber-optic multi-target multispectral pyrometer for achievable true temperature measurement of the solid rocket motor plume , 2017 .