A semi-implicit Chord Length Sampling method for dispersion fuel analysis

With the advent of stochastic geometry in nuclear reactors, implicit modeling processes play an increasingly important role in the precise simulation of particle transport in random media. Current implicit modeling methods in RMC utilize Chord Length Sampling (CLS). However, the CLS method experiences significant inaccuracies compared to explicit modeling methods when simulating materials of high scattering and absorbing properties, particularly where absorption interferes with scattering, and is especially prone to errors when simulating non-Markovian stochastic media. A Semi-Implicit CLS (SCLS) method is proposed where previous neutron and particle positions are recorded, while an inclusion sphere is used to maximize the accuracy of the method whilst minimizing the computational expense incurred. The accuracy of the algorithm was then verified against particle distributions generated via explicit modeling methods. The results show that SCLS can significantly improve the accuracy of implicit modeling when simulating non-Markovian dispersion fuel compared to the original CLS method.