发表机构
Ghent University（根特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文开发了基于有限体积法的动理学代码KOBRA，采用自适应网格细化（AMR）模拟托卡马克ELM输运，在保持精度的同时减少30-40%内存并加速至两倍。
AI 中文摘要
开发了一个基于有限体积法的动理学平行输运代码KOBRA，用于研究从赤道面到偏滤器靶板的边缘局域模（ELM）等离子体输运。德拜长度（约厘米量级）与连接长度（约10-20米）之间的巨大尺度分离导致全6维模拟的计算成本过高。为缓解这一问题，采用了自适应网格细化（AMR）策略。与均匀网格模拟的比较表明，AMR能够准确再现特征性的ELM动力学，包括偏滤器通量的快速上升和缓慢衰减，以及由快电子引起的早期峰值。对电子分布和自洽电场的分析揭示，AMR效率与相空间演化密切相关。总体而言，AMR在实现相当物理精度的同时，将内存使用量减少了30-40%，并将计算速度提升至多两倍，展示了其在高维动理学ELM模拟中的有效性。
英文摘要
A kinetic parallel transport code KOBRA based on a finite-volume method is developed to study edge localized mode (ELM) plasma transport from the mid-plane to divertor targets. The large scale separation between the Debye length (~cm) and the connection length (~10-20 m) leads to prohibitive computational cost in full 6D simulations. To alleviate this, an adaptive-mesh refinement (AMR) strategy is employed. Comparisons with uniform-grid simulations show that AMR accurately reproduces the characteristic ELM dynamics, including the rapid rise and slow decay of divertor fluxes, as well as the early-time peak induced by fast electrons. Analysis of the electron distribution and self-consistent electric field reveals that AMR efficiency is closely linked to phase-space evolution. Overall, AMR achieves comparable physical accuracy while reducing memory usage by 30-40% and accelerating computations by up to a factor of two, demonstrating its effectiveness for high-dimensional kinetic ELM simulations.
Comments7 pages, published in Contributions to Plasma Physics
Journal refC. Wang, S. Van Loo, and G. Verdoolaege, Modeling of Plasma Transport During Edge-Localized Modes in Tokamaks Using a Kinetic Vlasov-Poisson Code, Contributions to Plasma Physics (2026): e70156
