Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas
Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas
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DOI：
10.1088/1741-4326/ab7597
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发表时间：
2020-03
期刊：
影响因子：
3.3
通讯作者：
A. Piccione-;J. Berkery;S. Sabbagh;Y. Andreopoulos
中科院分区：
文献类型：
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作者：
A. Piccione-;J. Berkery;S. Sabbagh;Y. Andreopoulos
要实现在托卡马克装置中产生聚变能量的目标，最大的挑战之一是必须避免由于不稳定而造成的等离子体电流中断。中断事件表征和预测(DECAF)框架就是为此目的而开发的，它集成了许多可能导致中断的因果事件的物理模型。提出了两种不同的机器学习方法来改进去咖啡因中包含的动力学稳定性模型中的理想磁流体力学(MHD)无壁极限分量。首先，对于国家球环实验(NSTX)的大型平衡数据库，采用随机森林回归(RFR)，以重现DCON计算的无墙效应的等离子体势能变化。这种基于树的方法提供了对每个输入特征的重要性的分析，从而深入了解潜在的物理现象。其次，用DCON程序对一个完全连接的神经网络进行了计算训练，得到了一个改进的闭合形式的无壁极限方程，它是RFR所指示的相关等离子体参数的函数。该神经网络以理想磁流体物理理论为指导，将其扩展到NSTX实验数据的范围之外。估计的值已被纳入无咖啡因动力学稳定性模型，并针对一组实验上稳定和不稳定的排放进行了测试。此外，神经网络的结果被用来模拟仅使用实时可用量进行的实时稳定性评估。最后，对模型的可移植性进行了研究，通过在兆安球形托卡马克(MAST)上测试NSTX训练的算法，得到了令人鼓舞的结果。
One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects, , for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value of has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST).