Quasi-Photon Monte Carlo on Radiative Heat Transfer: An Importance Sampling Approach

Abstract The radiative heat transfer phenomenon is a complex process with various events of absorption, emission, and scattering of photon rays. Moreover, the effect of a participating medium adds to the complexity. Existing analytical methods fail to achieve accurate results with all such phenomena. In such cases, brute force algorithms such as the Monte Carlo Ray Tracing (MCRT) or the Photon Monte Carlo (PMC) has gained a lot of importance. But such processes, even if they provide less error than analytical methods, are quite expensive in computation time. Moreover, there are various shortcomings with traditional PMC in effectively including the nature of the participating medium and high variance in results. In this study, a modified PMC is simulated for a one-dimensional medium-surface radiation exchange problem. The medium is taken to be CO (4+) band system, and the behaviour is modelled by Importance Sampling (IS) of the spectrum data for variance reduction. Furthermore, PMC with low-discrepancy sequences like Halton, Sobol, and Faure sequences, known as Quasi-Monte Carlo (QMC), was simulated. QMC proved to be more efficient in reducing variance and computation time. Effective IS included with QMC is observed to have a much smaller variance and is faster as compared to traditional PMC.