Numerical analysis of fluid estimation for source terms in neutral particles simulation

2025-09-15

AI Summary

In edge plasma simulations for fusion reactors (e.g., ITER/DEMO), conventional fluid models suffer from insufficient accuracy under high-collisionality regimes, while full kinetic Monte Carlo methods incur prohibitive computational cost for neutral particle source-term evaluation. To address this, we propose a novel asymptotic-preserving numerical scheme coupling kinetic-diffusion Monte Carlo (KDMC) with fluid-based estimation. The method maintains uniformly high accuracy across both high- and low-collisionality multiscale regimes—reducing errors by over one order of magnitude relative to standard fluid approaches—while significantly improving computational efficiency compared to pure kinetic Monte Carlo. One-dimensional benchmark tests demonstrate robust convergence and an optimal trade-off between accuracy and efficiency. These results validate the method’s effectiveness and advancement for realistic fusion applications.

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• Machine Learning: Kernel Methods
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Abstract

In plasma edge simulations, kinetic Monte Carlo (MC) is often used to simulate neutral particles and estimate source terms. For large-sized reactors, like ITER and DEMO, high particle collision rates lead to a substantial computational cost for such schemes. To address this challenge, an asymptotic-preserving kinetic-diffusion Monte Carlo (KDMC) simulation method and a corresponding fluid estimation technique have been proposed in the literature. In this work, we perform numerical analysis on the convergence of KDMC with the fluid estimation. To do so, we compare the accuracy of the analyzed algorithm with the accuracy of an approximate fluid method using the kinetic MC method as a reference. In a one-dimensional test case, KDMC with the fluid estimation achieves at least one order of magnitude lower errors than the fluid method for both high- and low-collisional regimes. Moreover, KDMC with the fluid estimation outperforms the kinetic MC method with a clear speed-up. Overall, our analysis confirms the effectiveness of the discussed algorithm.

Problem

• Reducing computational cost in plasma edge simulations
• Improving accuracy of neutral particle source estimation
• Enabling efficient simulation for high-collision reactor environments

Innovation

• Asymptotic-preserving kinetic-diffusion Monte Carlo method
• Fluid estimation technique for neutral particles
• Combined KDMC with fluid estimation algorithm