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VEQDB:一个紧凑且可重构的多设备托卡马克平衡数据库

VEQDB: A Compact and Reconstructible Multi-Device Tokamak Equilibrium Database

Huasheng Xie, Ruohan Zhang, Xingyu Li, Feng Zhang, Zhengxiong Wang

arXiv 2609.23296首次发表:更新:

发表机构

Beijing VeloAlpha Technology Co., Ltd.; Key Laboratory of Materials Modification by Beams of the Ministry of Education, School of Physics, Dalian University of Technology(北京维洛阿尔法科技有限公司; 大连理工大学物理学院光束材料改性教育部重点实验室)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

VEQDB是一个基于连续MXH-Chebyshev几何的紧凑可重构托卡马克平衡数据库,提供13,291个平衡,实现89-96倍压缩,支持跨设备比较与降阶建模。

AI 中文摘要

托卡马克平衡通常以网格化的G-EQDSK文件形式交换,其约定、分辨率和特定于机器的格式阻碍了跨设备比较和数据驱动建模。在此,我们提出VEQDB,一个基于连续MXH--Chebyshev几何和独立物理剖面根构建的开放、紧凑且可重构的固定边界平衡数据库。通过将权威平衡物理与矩形网格解耦,VEQDB能够在任意应用所需的分辨率下进行连续评估和度量区分。在自动化数值验证流程的支持下,VEQDB被构建为一个可扩展的存储库,用于持续的社区扩展。其首次发布提供了跨越267个常规和球形托卡马克的13,291个已接受平衡,包括参数采样的Grad--Shafranov解、涵盖EAST、MAST-U和ITER尺度的G-EQDSK投影,以及具有明确来源的可控变化族。基准投影以$1.09 \ imes 10^{-3}$至$1.45 \ imes 10^{-3}$的均方根误差重现归一化通量图,而紧凑的JSON表示相对于标准$129 \ imes 129$ G-EQDSK文件实现了89至96倍的尺寸缩减。完整的初始发布占用原始JSON格式41~MB和压缩归档18~MB,所有记录均成功通过了独立的重新加载和评估测试。VEQDB为平衡研究、降阶代理建模和跨机器工作流建立了一个可扩展、保留来源的基础。

英文摘要

Tokamak equilibria are commonly exchanged as gridded G-EQDSK files whose conventions, resolutions, and machine-specific formats impede cross-device comparisons and data-driven modeling. Here, we present VEQDB, an open, compact, and reconstructible fixed-boundary equilibrium database built on continuous MXH--Chebyshev geometry and independent physical-profile roots. By decoupling authoritative equilibrium physics from rectangular meshes, VEQDB enables continuous evaluation and metric differentiation at arbitrary application-demanded resolutions. Backed by an automated numerical validation pipeline, VEQDB is structured as an extensible repository for ongoing community expansion. Its inaugural release provides 13,291 accepted equilibria across 267 conventional and spherical tokamaks, encompassing parameter-sampled Grad--Shafranov solutions, G-EQDSK projections spanning EAST, MAST-U, and ITER scales, and controlled variation families with explicit provenance. Benchmark projections reproduce normalized flux maps with RMS errors between $1.09 \times 10^{-3}$ and $1.45 \times 10^{-3}$, while compact JSON representations achieve an 89--96-fold size reduction relative to standard $129 \times 129$ G-EQDSK files. The complete initial release occupies 41~MB in raw JSON and 18~MB in compressed archives, and all records successfully passed independent reload and evaluation tests. VEQDB establishes an extensible, provenance-preserving foundation for equilibrium studies, reduced-order surrogate modeling, and cross-machine workflows.

Comments17 pages, 5 figures

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