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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.01984 |
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Table of Contents:
- The Monte Carlo method is typically considered the gold standard for simulating reactor physics problems, as it does not require discretization of the phase space. This is not necessarily true though when simulating multigroup problems, as it has traditionally been a challenge to model anisotropic scattering in such simulations. Multigroup data used in reactor simulations generally uses low order Legendre expansion for scattering distributions, often stopping at the third Legendre moment. With so few terms, the angular distribution can easily have negative regions for highly anisotropic energy transfers, which makes it impossible to use standard Monte Carlo methods to sample a scattering angle. Multigroup Monte Carlo codes therefore often resort to only using isotropic scattering with the transport correction (which is not always possible), or approximating the angular distribution with discrete angles. Neither case is ideal, and makes it impossible to accurately model such problems or verify deterministic codes that can and do make use of the low order Legendre expansions without issue. This is addressed in the present study by using importance sampling in conjunction with negative particle weights to sample scattering angles from negative scattering distributions. It is demonstrated that such an approach necessitates the use of weight cancellation methods in order to be stable and converge to a solution. The technique is tested on two simple analytic benchmark problems, and then further demonstrated by modeling a small zero power research reactor, comparing results against a deterministic solver which can treat anisotropic scattering. Comparison of the simulation results indicates that importance sampling for anisotropic scattering with weight cancellation can be used to obtain reference Monte Carlo results for multigroup problems despite negative scattering distributions.