David R. Hatch, Craig Michoski, Dongyang Kuang, Ben Chapman-Oplopoiou, Max Curie, Michael Halfmoon, Ehab Hassan, Mike Kotschenreuther, Swadesh M. Mahajan, Gabri… 展开作者
David R. Hatch, Craig Michoski, Dongyang Kuang, Ben Chapman-Oplopoiou, Max Curie, Michael Halfmoon, Ehab Hassan, Mike Kotschenreuther, Swadesh M. Mahajan, Gabriele Merlo, M. J. Pueschel, Justin Walker, Cole D. Stephens
This paper reports on the development of reduced models for electron temperature gradient (ETG) driven transport in the pedestal. Model development is enabled by a set of 61 nonlinear gyrokinetic simulations with input parameters taken from the pedestals in a broad range of experimental scenarios. The simulation data has been consolidated in a new database for gyrokinetic simulation data, the Multiscale Gyrokinetic Database (MGKDB), facilitating the analysis. The modeling approach may be considered a generalization of the standard quasilinear mixing length procedure. The parameter eta, the ratio of the density to temperature gradient scale length, emerges as the key parameter for formulating an effective saturation rule. With a single order-unity fitting coefficient, the model achieves an RMS error of 15%. A similar model for ETG particle flux is also described. We also present simple algebraic expressions for the transport informed by an algorithm for symbolic regression.
Modeling Temperature Profiles in the Pedestal of NSTX with Reduced Models
Institute for Fusion Studies, The University of Texas at Austin（德克萨斯大学奥斯汀分校聚变研究所）；ExoFusion；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）；Princeton Plasma Physics Laboratory（普林斯顿等离子体物理实验室）
Machine learning methods for modelling local, linear gyrokinetic simulations of MAST-U pedestal turbulence
VTT Technical Research Centre of Finland（芬兰技术研究中心）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Institute for Fusion Studies, University of Texas at Austin（德克萨斯大学奥斯汀分校聚变研究所）；UK Atomic Energy Authority（英国原子能管理局）
Strong gradient neoclassical transport in the plateau regime
Princeton Plasma Physics Laboratory（普林斯顿等离子体物理实验室）；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）；Laboratorio Nacional de Fusión, CIEMAT（CIEMAT国家聚变实验室）
Machine Learning for Electron-Scale Turbulence Modeling in W7-X
Department of Mathematics and Division of Computational Modeling and Data Analytics, Academy of Data Science, Virginia Tech（数学系和计算建模与数据科学学院，数据科学学院，弗吉尼亚理工学院）；Max Planck Institute for Plasma Physics（最大平面物理研究所）
