MGKDB：一种与IMAS对齐的多代码 gyrokinetic 模拟数据库，用于可复现的聚变湍流建模和数据驱动分析

中文摘要 AI 辅助

昂贵的聚变模拟通常以特定代码的格式保存，这限制了发现、比较和复用。我们推出多尺度 gyrokinetic 数据库（MGKDB），这是一个开源软件框架和精选档案，可将异构模拟活动转换为可追溯的科学记录。每条记录将代码原生输入输出与溯源、质量元数据、与 IMAS 对齐的物理表示以及衍生诊断工具关联，在保留特定模型证据的同时支持通用领域查询。生产路径支持线性和非线性 GENE 与 CGYRO 计算，以及简化的准线性 TGLF 评估。截至 2026 年 9 月 1 日的快照，MGKDB 包含 1068089 条记录，几乎所有记录都包含已填充的 gyrokinetics IMAS 分支。该软件公开可用，而对 NERSC 托管的生产记录的访问受管理。三个演示表明，这些关联表示如何支持科学复用：诊断分支中存储的标准化量支持对存档线性模式的种群规模分析；通用输入坐标揭示多代码集合的覆盖范围、冗余度和活动驱动的采样结构；记录级别的原生 CGYRO 输入检索驱动匹配的 TGLF 计算，并为探索性代理建模生成可追溯数据集。这些示例共同证明，MGKDB 支持档案表征、候选跨代码和跨保真度比较、活动规划以及可复现的数据驱动建模，且不会自动将不同模型视为等价。

英文摘要

Expensive fusion simulations are commonly preserved in code-specific formats that limit discovery, comparison, and reuse. We present the Multiscale GyroKinetic DataBase (MGKDB), an open-source software framework and curated archive that converts heterogeneous simulation campaigns into traceable scientific records. Each record links code-native inputs and outputs to provenance and quality metadata, an IMAS-aligned physics representation, and derived diagnostics, preserving model-specific evidence while enabling common-field queries. Production pathways support linear and nonlinear GENE and CGYRO calculations and reduced quasilinear TGLF evaluations. At the September 1, 2026 snapshot, MGKDB contained 1,068,089 records, nearly all of which included a populated gyrokinetics IMAS branch. The software is openly available, while access to the NERSC-hosted production records is managed. Three demonstrations show how these linked representations support scientific reuse. Standardized quantities stored in the Diagnostics branch enable population-scale analysis of archived linear modes; common input coordinates reveal coverage, redundancy, and campaign-driven sampling structure across a multicode collection; and record-level retrieval of native CGYRO inputs drives matched TGLF calculations and produces a traceable dataset for exploratory surrogate modeling. Together, these examples demonstrate how MGKDB supports archive characterization, candidate cross-code and cross-fidelity comparisons, campaign planning, and reproducible data-driven modeling without treating different models as automatically equivalent.

发表机构

• Sophelio LLC（索菲奥有限责任公司）
• The University of Texas at Austin（德克萨斯大学奥斯汀分校）
• University of California San Diego（加州大学圣地亚哥分校）
• Dutch Institute for Fundamental Energy Research（荷兰基础能源研究所）
• Eindhoven University of Technology（埃因霍温理工大学）
• Ruhr-Universität Bochum（波鸿鲁尔大学）
• Plasma Science and Fusion Center, Massachusetts Institute of Technology（麻省理工学院等离子体科学与聚变中心）
• General Atomics（通用原子能公司）

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