Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning
C. S. Chang, S. Ku, R. Hager, R. M. Churchill, J. Hughes, F. K\"ochl,, A. Loarte, V. Parail, R. Pitts

TL;DR
This paper develops a new predictive scaling formula for ITER's divertor heat-load width using machine learning informed by gyrokinetic simulations, improving accuracy across different tokamaks and ITER scenarios.
Contribution
A machine learning-based modification to the Eich formula for predicting divertor heat-load width, validated on multiple tokamaks and ITER scenarios, incorporating physics of trapped-electron-mode turbulence.
Findings
The new formula reproduces existing scaling laws for current tokamaks.
It predicts wider heat-load widths for ITER Q=10 plasma.
Validation on multiple ITER scenarios confirms its robustness.
Abstract
Understanding and predicting divertor heat-load width is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predictive simulation data for using the extreme-scale edge gyrokinetic code XGC1 in the electrostatic limit under attached divertor plasma conditions in three major US tokamaks [C.S. Chang et al., Nucl. Fusion 57, 116023 (2017)] reproduced the Eich and Goldston attached-divertor formula results [formula #14 in T. Eich et al., Nucl. Fusion 53, 093031 (2013); R.J. Goldston, Nucl. Fusion 52, 013009 (2012)], and furthermore predicted over six times wider than the maximal Eich and Goldston formula predictions on a full-power (Q = 10) scenario ITER plasma. After adding data from further predictive simulations on a highest current JET and highest-current Alcator C-Mod, a machine…
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