Neural network based fast prediction of β _N limits in HL-2M

Abstract

The artificial neural networks (NNs) are trained, based on the numerical database, to predict the no-wall and ideal-wall βN limits, due to onset of the n = 1 (n is the toroidal mode number) ideal external kink instability, for the HL-2M tokamak. The database is constructed by toroidal computations utilizing both the equilibrium code CHEASE and the stability code MARS-F. The stability results show that (i) the plasma elongation generally enhances both βN limits, for either positive or negative triangularity plasmas; (ii) the effect is more pronounced for positive triangularity plasmas; (iii) the computed no-wall βN limit linearly scales with the plasma internal inductance, with the proportionality coefficient ranging between 1 and 5 for HL-2M; (iv) the no-wall limit substantially decreases with increasing pressure peaking factor. Furthermore, both the Neural Network (NN) model and the Convolutional Neural Networks model (CNN) are trained and tested, resulting in consistent results. The trained NNs predict both the no-wall and ideal-wall limits with as high as 95% accuracy, compared to those directly computed by the stability code. Additional test cases, produced by the Tokamak Simulation Code (TSC), also show reasonable performance of the trained NNs, with the relative error being within 10%. The constructed database provides effective references for the future HL-2M operations. The trained NNs can be used as a real-time monitor for disruption prevention in the HL-2M experiments, or serve as part of the integrated modeling tools for ideal kink stability analysis.

Surrogate models for plasma displacement and current in 3D perturbed magnetohydrodynamic equilibria in tokamaks

“…As for the NN input, we largely follow the recent work [42,43] where NNs were trained to predict the no-wall Troyon pressure limits due to onset of low-n ideal external kink instabilities. In order for the NN to be able to predict the perturbed 3D equilibrium, the essential information on the 2D equilibrium has to be provided as the input.…”

Deep learning approaches to recover the plasma current density profile from the safety factor based on Grad–Shafranov solutions across multiple tokamaks

“…We achieve this by utilizing machine learning (ML) techniques based on a numerically generated equilibrium database. Machine learning has been widely used in fusion research, for example in plasma instability and disruption predictions [3][4][5][6][7][8][9][10][11][12][13][14], magnetic control [15], non-powerlaw scaling [16], turbulent transport [17][18][19] and plasma profile predictions [20,21], as well as fast magnetic equilibrium reconstruction [22][23][24][25][26][27].…”