A kinetic-diffusion Monte Carlo-based particle-level fluid-kinetic decomposition for neutral transport simulations

Abstract: Neutrals in the plasma edge are commonly modeled by kinetic equations, with quantities of interest given by macroscopic quantities such as density, velocity, and temperature. In reactor-relevant regimes, fully kinetic descriptions solved by Monte Carlo (MC) methods, although accurate, become computationally expensive, whereas fluid-limit approximations are computationally more efficient but may lose accuracy due to boundary effects or low-collisional regimes. Hybrid fluid-kinetic approaches aim to combine the strengths of both descriptions. However, existing simulation methods face challenges, including interface handling in domain decomposition, unphysical assumptions, and iterative coupling in distribution decomposition. In this work, we propose a distribution-decomposition hybrid model constructed at the particle level based on the kinetic-diffusion Monte Carlo (KDMC) method. The model inherits key properties of KDMC: it is asymptotic-preserving and does not require iterative coupling between the fluid and kinetic components. To improve the accuracy of the fluid-part quantities estimation, a Navier-Stokes-type fluid system is derived via Hilbert-Chapman-Enskog expansions, tailored for KDMC. In the considered one-dimensional tests, the resulting fluid system has comparable accuracy to the AFN model used in SOLPS-ITER while requiring substantially fewer nonlinear iterations. Additionally, a tunable reflective boundary condition is introduced that allows balancing accuracy and efficiency. The model exhibits at least 500 times speedup over the kinetic MC, while maintaining relative L2 errors around 10% in a charge exchange (CX)-dominant test case. In non-CX-dominant regimes, the accuracy becomes increasingly sensitive to boundary treatment due to the inherent limitations of the fluid approximation near the boundary, motivating further refinement of the KDMC boundary conditions.