Data-driven profile prediction for DIII-D

被引:28
作者
Abbate, J. [1 ,2 ]
Conlin, R. [2 ,3 ]
Kolemen, E. [2 ,3 ]
机构
[1] Princeton Univ, Dept Astrophys Sci, Princeton, NJ 08544 USA
[2] Princeton Plasma Phys Lab, Princeton, NJ 08543 USA
[3] Princeton Univ, Dept Mech & Aerosp Engn, Princeton, NJ 08544 USA
关键词
DIII-D; neural networks; transport; control; TRANSPORT; DISRUPTIONS;
D O I
10.1088/1741-4326/abe08d
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
A new, fully data-driven algorithm has been developed that uses a neural network to predict plasma profiles on a scale of tau(E) into the future given an actuator trajectory and the plasma state history. The model was trained and tested on DIII-D data from the 2013-2018 experimental campaigns. The model runs in tens of milliseconds and is very simple to use. This makes it a potentially useful tool for operators and physicists when planning plasma scenarios. It is also fast enough to be used for real-time model-predictive control.
引用
收藏
页数:12
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