Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

Piccione, A.; Berkery, J. W.; Sabbagh, S. A.; Andreopoulos, Y.

doi:10.1088/1741-4326/ab7597

Title: Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

Journal Article · Wed Mar 18 00:00:00 EDT 2020 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/ab7597· OSTI ID:1803428

^[1]; Berkery, J. W. ^[2]; Sabbagh, S. A. ^[2]; Andreopoulos, Y. ^[1]

Univ. College London (United Kingdom). Dept. of Electronic and Electrical Engineering
Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics

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 β 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 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).

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Research Organization:: Columbia Univ., New York, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: SC0018623

OSTI ID:: 1803428

Journal Information:: Nuclear Fusion, Vol. 60, Issue 4; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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