Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning

Understanding and predicting divertor heat-load width ${\lambda}_q$ is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predictive simulation data for ${\lambda}_q$ 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 ${\lambda}_q$ 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 learning program is used to identify a new scaling formula for ${\lambda}_q$ as a simple modification to the Eich formula #14, which reproduces the Eich scaling formula for the present tokamaks and which embraces the wide ${\lambda}_q^X{GC}$ for the full-current Q = 10 ITER plasma. The new formula is then successfully tested on three more ITER plasmas: two corresponding to long burning scenarios with Q = 5 and one at low plasma current to be explored in the initial phases of ITER operation. The new physics that gives rise to the wider ${\lambda}q_^{XGC} is identified to be the weakly-collisional, trapped-electron-mode turbulence across the magnetic separatrix, which is known to be an efficient transporter of the electron heat and mass. Electromagnetic turbulence and high-collisionality effects on the new formula are the next study topics for XGC1.