Data-driven profile prediction for DIII-D

被引：30

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

来源：

NUCLEAR FUSION | 2021年 / 61卷 / 04期

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.

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页数：12

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