The rank correlated FSK model for prediction of gas radiation in non-uniform media, and its relationship to the rank correlated SLW model. (July 2018) - Solovjov, Vladimir P.; Webb, Brent W.; Andre, Frederic
Highlights: A new full spectrum rank correlated k -distribution method (RC-FSK) for modeling radiation transfer in non-uniform high temperature gaseous media is proposed. Implementation of the RC-FSK spectral model does not require specification of a reference gas thermodynamic state. The work presents predictions using the new Rank Correlated FSK method and previous FSK formulations in three different example problems. The work reveals that the proposed RC-FSK model is theoretically identical to the recently developed Rank Correlated Spectral Line Weighted-sum-of-gray-gases model (RC-SLW). Abstract: Following previous theoretical development based on the assumption of a rank correlated spectrum, the Rank Correlated Full Spectrum k -distribution (RC-FSK) method is proposed. The method proves advantageous in modeling radiation transfer in high temperature gases in non-uniform media in two important ways. First, and perhaps most importantly, the method requires no specification of a reference gas thermodynamic state. Second, the spectral construction of the RC-FSK model is simpler than original correlated FSK models, requiring only two cumulative k -distributions. Further, although not exhaustive, example problems presented here suggest that the method may also yield improved accuracy relative to prior methods, and may exhibit less sensitivity to the blackbody source temperature used in the model predictions. This paper outlines the theoretical development of the RC-FSK method, comparing the spectral construction with prior correlated spectrum FSK method formulations. Further the RC-FSK model's relationship to the Rank Correlated Spectral Line Weighted-sum-of-gray-gases (RC-SLW) model is defined. The work presents predictions using the Rank Correlated FSK method and previous FSK methods in three different example problems. Line-by-line benchmark predictions are used to assess the accuracy.