An inversion of plume gas concentration distribution based on multivariate regression analysis

Qing Ye; Xiaoquan Sun; Li Shao; Yafu Wang

doi:10.1117/12.837910

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20 November 2009 An inversion of plume gas concentration distribution based on multivariate regression analysis

Qing Ye; Xiaoquan Sun; Li Shao; Yafu Wang

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Proceedings Volume 7511, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems; 75110L (2009) https://doi.org/10.1117/12.837910
Event: International Conference on Optical Instrumentation and Technology, 2009, Shanghai, China

Abstract

In light of the difficulties to directly measure plume gas concentration by existing methods, the paper proposed an inversion algorithm based on multivariate regression analysis. We first of all built up a multivariate regression model of plume gas concentration by dividing the plume into several homogeneous layers along the observation direction. Then a group of discrete spectral data was sampled out from plume infrared radiation curve at the intervals of certain wave numbers. Thus the spectroscopic data without atmospheric attenuation could be obtained when the discrete spectral data was divided by the atmospheric transmittances at corresponding wave numbers. After that, we worked on the temperature profile of the plume, figuring out the average temperature of each layer of plume through integration according to the outcomes of plume layering. At the same time, supported by the High Resolution Temperature Gas Spectral Database (HITEMP), we also computed out the average absorption coefficient of each layer of plume. Thereby, the triplicity of the spectroscopic data without atmospheric attenuation, the average temperature of each layer of plume and the average absorption coefficient of each layer of plume, as the input parameters for the multivariate regression model of plume gas concentration, could finally enable us to work out the concentration distribution of the plume gas along the observation direction by least squares method which, however, only took into consideration the effect of vapor and carbon dioxide. The comparison with the concentration distribution acquired through numerical computation of plume flow field proves the feasibility of the inversion algorithm.

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Qing Ye, Xiaoquan Sun, Li Shao, and Yafu Wang "An inversion of plume gas concentration distribution based on multivariate regression analysis", Proc. SPIE 7511, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 75110L (20 November 2009); https://doi.org/10.1117/12.837910

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