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

2026-06-22

AI Summary

This work addresses the challenges in simulating neutral particle transport in plasma edge regions under reactor-relevant conditions, where fully kinetic Monte Carlo methods incur prohibitive computational costs and fluid approximations lack accuracy in low-collisionality regimes. The authors propose a particle-based hybrid fluid-kinetic model grounded in Kinetic-Diffusion Monte Carlo (KDMC), which employs a decomposition of the distribution function to achieve an asymptotic-preserving algorithm without iterative coupling. By leveraging a Hilbert–Chapman–Enskog expansion, they derive a Navier–Stokes-type fluid system tailored to KDMC and introduce adjustable reflection boundary conditions. One-dimensional benchmarks demonstrate that the method achieves over 500-fold speedup compared to pure kinetic simulations, with L2 relative errors of approximately 10% in charge-exchange-dominated scenarios; however, accuracy becomes highly sensitive to boundary treatment when charge exchange is not dominant.

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.

Problem

• neutral transport
• fluid-kinetic hybrid
• Monte Carlo simulation
• boundary conditions
• computational efficiency

Innovation

• kinetic-diffusion Monte Carlo
• fluid-kinetic hybrid
• asymptotic-preserving
• distribution decomposition
• reflective boundary condition