Equilibrium reconstruction is crucial to the operation and analysis of tokamak experiments, including the validation of advanced theory models. Machine learning can improve the speed and accuracy by using a probabilistic framework to not only predict the flux based on magnetic measurements, but the accuracy of that prediction. Green’s function separation is used to create a device-independent surrogate model that predicts H-mode pedestals from magnetics-only data. These results required creating a large, advanced database for machine-learning training.

Verification calculations of several widely used three-dimensional (3D) magnetohydrodynamic (MHD) equilibrium codes were performed in strongly shaped configurations; for both fixed and free-boundary equilibria; for the so-called vacuum case and for the case of high plasma pressure; and, for magnetic fields that have nested magnetic surfaces and for fields that have a magnetic island. A new method was introduced for constructing coordinates in arbitrarily shaped toroidal domains, and this can improve the robustness of 3D equilibrium calculations. Beyond the equilibrium calculation, the capability of performing non- linear extended-MHD modelling of optimized stellarator equilibria was demonstrated.

Energetic particle (EP) confinement properties of ITER operation scenarios has been comprehensively assessed using global gyrokinetic codes, hybrid MHD codes, and reduced EP transport models. Existing DIII-D tokamak data were used for validation and additional new DIII-D experiments were also performed. A total of 12 state-of-the-art energetic particle (EP) codes from ITER partnerships (US, EU, Asia) have participated in this collaboration to comprehensively assess EP confinement properties of ITER operation scenarios using global gyrokinetic, kinetic-MHD, and reduced EP transport models. 

Building understanding by accurate simulation of the runaway electron seed generation during thermal collapse disruptions in tokamaks is critically important to being able to avoid and or mitigate their generation. Analysis of disruption scenarios generally involves the physics of the disrupting plasma, the relativistic kinetic physics of the driven runaways, and the substantial complexity of the computational methods involved in their simulation. In this project, simulations were performed with a suite of codes including kinetic and magnetohydrodynamic (MHD) models, to investigate the generation of the seed runaway electrons and their subsequent acceleration.  

Asymmetrical vessel forces, sometimes called horizontal forces or “sideways forces”, arise from 3D kinking motions of the plasma, usually during the late stages of the event. These plasma motions can induce and conduct 3D current patterns into the surrounding vessel which interact with the toroidal field to produce asymmetrical forces. Previous papers predict large values for these forces in ITER. The goal of the present milestone is to calculate these sideways forces directly from a 3D MHD simulation using realistic values for the wall resistivity in ITER.

Understanding the relevant turbulent transport mechanisms at the edge of a high-performance tokamak is essential for predicting and optimizing the H-mode pedestal structure in future burning plasma devices. Global electromagnetic gyrokinetic simulations will be performed based on representative experimental pedestal scenarios in order to clarify which instabilities are most important for each of the particle and heat transport channels. Edge transport modeling will be performed in order to estimate and bound the particle and heat sources—e.g., the ionization density source and the atomic energy loss channels due to ionization, charge exchange, and radiation. Comparisons will be made with data from the DIII-D, JET, C-Mod and NSTX or MAST experiments.

The interaction of the boundary plasma with the material surfaces in magnetically confined plasmas is among the most critical problems in fusion energy science. In FY2018, perform high performance computing simulations with coupled boundary plasma physics and materials surface models to predict the fuel recycling and tritium retention of the ITER divertor for D-T burning plasma conditions, accounting for erosion, re-deposition and impurity transport in the plasma boundary, and an initial evaluation of the influence of material deposition on the recycling and retention.

Lower hybrid current drive (LHCD) will be indispensable for driving off-axis current during long-pulse operation of future burning plasma experiments including ITER, since it offers important leverage for controlling damaging transients caused by magnetohydrodynamic instabilities. However, the experimentally demonstrated high efficiency of LHCD is incompletely understood. In FY 2017, massively parallel, high-resolution simulations with 480 radial elements and 4095 poloidal modes will be performed using full-wave radiofrequency field solvers and particle Fokker-Planck codes to elucidate the roles of toroidicity and full-wave effects. The simulation predictions will be compared with experimental data from the superconducting EAST tokamak.

Predicting the magnitude and scaling of the divertor heat load width in magnetically confined burning plasmas is a high priority for the fusion program and ITER. One of the key unresolved physics issues is what sets the heat flux width. Perform massively parallel simulations using 3D edge kinetic and fluid codes to compute and determine the parameter dependence of the heat load width on the divertor plates applicable to moderate particle recycling conditions. Comparisons will be made with data from DIII-D, NSTX-U, and C-Mod.