Integrated Tokamak modeling: When physics informs engineering and research planning

Abstract

Modeling tokamaks enables a deeper understanding of how to run and control our experiments and how to design stable and reliable reactors. We model tokamaks to understand the nonlinear dynamics of plasmas embedded in magnetic fields and contained by finite size, conducting structures, and the interplay between turbulence, magneto-hydrodynamic instabilities, and wave propagation. This tutorial guides through the components of a tokamak simulator, highlighting how high-fidelity simulations can guide the development of reduced models that can be used to understand how the dynamics at a small scale and short time scales affects macroscopic transport and global stability of plasmas. It discusses the important role that reduced models have in the modeling of an entire plasma discharge from startup to termination, the limits of these models, and how they can be improved. It discusses the important role that efficient workflows have in the coupling between codes, in the validation of models against experiments and in the verification of theoretical models. Finally, it reviews the status of integrated modeling and addresses the gaps and needs towards predictions of future devices and fusion reactors.

The role of edge resonant magnetic perturbations in edge-localized-mode suppression and density pump-out in low-collisionality DIII-D plasmas

“…Here we do not consider resonant field penetration at the foot of the pedestal. To do so we would need a more accurate model of the ITER profiles near the separatrix, and this is also an area of active research [75].…”

Reduced energetic particle transport models enable comprehensive time-dependent tokamak simulations

“…A reduced approach for codes such as TRANSP is presently under consideration. In spite of these difficulties, reduced models already represent an effective alternative to first-principles codes for scenario development, when accuracy in the prediction of EP-driven instabilities and associated transport is sacrificed in favor of the integration of those effects into the bigger picture of integrated modeling [43].…”

Fast modeling of turbulent transport in fusion plasmas using neural networks

“…Accurate prediction of tokamak core plasma temperature and density is essential for interpretation and preparation of current-day fusion experiments, optimization of plasma scenarios, and designing future devices. Time-evolved tokamak simulation on discharge time scales is typically carried out within an "integrated modeling" approach, 1 where multiple models representing various physics phenomena are coupled together within a single code or workflow. An essential component of integrated models is the prediction of turbulent fluxes, particularly in the tokamak core where transport is often dominated by plasma microinstabilities.…”