Full-spectrum correlated-k-distribution look-up table for use with radiative Monte Carlo solvers. (March 2019)

• Record Type: Journal Article
• Authors: Wang, Chaojun; Modest, Michael F.; He, Boshu
• Is Part Of: International journal of heat and mass transfer. Volume 131(2019)
• Page Start: 167
• Page End: 175
• Publication Date: 2019-03
• Subjects: Radiative heat transfer -- Full-spectrum correlated-k-distribution (FSCK) -- Photon Monte Carlo (PMC) -- Look-up table -- Random-number database -- Nonhomogeneous media; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2018.10.133
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: A random-number relation was derived for the use of FSCK/PMC method. A random-number database was constructed for gas-soot mixtures. Both accuracy and efficiency of the random-number database were verified. Memory requirement of the LBL/PMC and FSCK/PMC methods were compared. Abstract: The full-spectrum correlated- k -distribution (FSCK) look-up table previously developed by the authors Wang et al. (2018) provides an efficient means for accurate calculations of radiative properties in nonhomogeneous media in conjunction with conventional radiative transfer equation (RTE) solvers. However, the current FSCK look-up table cannot be combined with statistical RTE solvers. A random-number relation was derived using the new implementation proposed in Wang et al. (2018) for the use of FSCK tables together with photon Monte Carlo (PMC) RTE solvers, and a corresponding random-number database including CO2, H2 O, CO and soot has been constructed. Radiative heat transfer for two realistic scaled flames is studied to test the performance of the FSCK/PMC method using both FSCK look-up table and random-number database. Results show that the FSCK/PMC method can achieve almost the same accuracy but at cheaper computational cost and much lower memory requirement compared to the benchmark line-by-line solutions.