Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities and compatible with ITER IMAS
Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities and compatible with ITER IMAS
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DOI：
10.1088/1741-4326/abb918
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发表时间：
2020-12
期刊：
影响因子：
3.3
通讯作者：
O. Meneghini;G. Snoep;B. Lyons;J. McClenaghan;C. Imai;B. Grierson;S.P. Smith;G. Staebler;
中科院分区：
文献类型：
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作者：
O. Meneghini;G. Snoep;B. Lyons;J. McClenaghan;C. Imai;B. Grierson;S.P. Smith;G. Staebler;
集成的建模工作流程能够找到具有自一致核心传输、基座结构、电流分布和等离子体平衡物理的稳态等离子体解决方案，并针对DIII-D放电进行了测试。实现的核心-基座耦合工作流程的主要特点是能够自一致地解释等离子体中杂质的传输，以及使用机器学习加速模型用于基座结构和湍流传输物理。值得注意的是，耦合工作流是在集成任务的一个建模框架（OMFIT）框架内实现的，并利用ITER集成建模和分析套件数据结构在模拟中涉及的物理代码之间交换数据。这种技术进步是由一种名为有序多维数组结构的新数字库的发展所促进的。
An integrated modeling workflow capable of finding the steady-state plasma solution with self-consistent core transport, pedestal structure, current profile, and plasma equilibrium physics has been developed and tested against a DIII-D discharge. Key features of the achieved core-pedestal coupled workflow are its ability to account for the transport of impurities in the plasma self-consistently, as well as its use of machine learning accelerated models for the pedestal structure and for the turbulent transport physics. Notably, the coupled workflow is implemented within the One Modeling Framework for Integrated Tasks (OMFIT) framework, and makes use of the ITER integrated modeling and analysis suite data structure for exchanging data among the physics codes that are involved in the simulations. Such technical advance has been facilitated by the development of a new numerical library named ordered multidimensional arrays structure.