Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

Sánchez-Villar, Á.; Bai, Z.; Bertelli, N.; Bethel, E. W.; Hillairet, J.; Perciano, T.; Shiraiwa, S.; Wallace, G. M.; Wright, J. C.

doi:10.1088/1741-4326/ad645d

Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

Journal Article · Mon Aug 12 00:00:00 EDT 2024 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/ad645d· OSTI ID:2429018

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Abstract

A real-time capable core Ion Cyclotron Range of Frequencies (ICRF) heating model on NSTX and WEST is developed. The model is based on two nonlinear regression algorithms, the random forest ensemble of decision trees and the multilayer perceptron neural network. The algorithms are trained on TORIC ICRF spectrum solver simulations of the expected flat-top operation scenarios in NSTX and WEST assuming Maxwellian plasmas. The surrogate models are shown to successfully capture the multi-species core ICRF power absorption predicted by the original model for the high harmonic fast wave and the ion cyclotron minority heating schemes while reducing the computational time by six orders of magnitude. Although these models can be expanded, the achieved regression scoring, computational efficiency and increased model robustness suggest these strategies can be implemented into integrated modeling frameworks for real-time control applications.

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

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2429018

Alternate ID(s):: OSTI ID: 2403560
OSTI ID: 2432518
OSTI ID: 2460471

Journal Information:: Nuclear Fusion, Journal Name: Nuclear Fusion Journal Issue: 9 Vol. 64; ISSN 0029-5515

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: IAEA

Language:: English

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Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

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Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

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