Remote Sensing of High Temperature H2O–CO2 Mixture with the Multi-Spectral Correlated-k Distribution Model

Abstract

Radiative heat transfer of gas plays an important role in remote sensing applications. Several models have been proposed to estimate the radiative properties of gases. The Line-By-Line (LBL) approach is the most accurate one. However, The Line-By-Line (LBL) approach usually requires excessive computation cost which makes it not applicable for most industrial most applications. Nevertheless, it still plays the role of benchmark reference for the assessment of other models. In this work, we propose the application of the Multi-Spectral Correlated-k distribution model (MSCk) so as to make the simulation of hot jet signature at long distance. In the MSCk model, the breakdown of correlation assumption used in the original Ck model for non-uniform media is overcome by introducing the clustering of scaling functions. The principle of MSCk model is to group together wavenumbers with respect to the spectral scaling functions-defined as the ratio between spectral absorption coefficients in distinct states-so that the correlation assumption can be considered as relatively exact over the corresponding intervals of wavenumbers. Results show that the MSCk model demonstrates better performance compared with the traditional Ck model at nearly an equivalent calculation cost.