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Development of a Core through SOL Impurity Transport Modeling Workflow Using Surrogate-Based Optimization

ORAL

Abstract

A novel workflow has been developed to self-consistently model impurity transport through the core and scrape-off layer of tokamak plasmas. Although transport in both of these regions is naturally coupled, differing physical processes mean that no one model can simulate the entire plasma. This is despite important, and often contradictory, requirements for performance. The workflow presented here uses ImpRad [1] in the core and SOLPS-ITER [2] in the scrape-off layer and divertor to interpret the impurity transport throughout an entire plasma discharge. In this workflow, Bayesian optimization is first used to match SOLPS-ITER simulations to desired plasma parameters. Then, the outputs of those simulations are used to constrain ImpRad modeling, allowing for a self-consistent description of the transport in both regions. The workflow is applied to a DIII-D H-mode discharge using an ITER-similar shape, matching simulated plasma conditions to measurements from the Thomson scattering, Langmuir probe, charge exchange recombination, and tangential TV diagnostics. Details of the workflow and results from the application to this discharge will be presented.



[1] F. Sciortino et al 2021 Plasma Phys. Control. Fusion 63 112001

[2] S. Wiesen et al 2015 J. Nucl. Mater. 463

Presenters

  • Ivan James Marshall

    Massachusetts Institute of Technology

Authors

  • Ivan James Marshall

    Massachusetts Institute of Technology

  • Nathan T Howard

    Massachusetts Institute of Technology, MIT PSFC

  • Pablo Rodriguez-Fernandez

    MIT PSFC

  • Rebecca L Masline

    Massachusetts Institute of Technology

  • A Stephane BIWOLE

    Massachusetts Institute of Technology, Massachusetts Institute of Technology, Boston, MA, United States of America

  • Leonardo Corsaro

    Massachusetts Institute of Technology

  • Marco Andrés Miller

    MIT Plasma Science and Fusion Center, Massachusetts Institute of Technology

  • Filippo Scotti

    Lawrence Livermore National Laboratory

  • Huiqian Wang

    General Atomics

  • Michael Robert Knox Wigram

    Massachusetts Institute of Technology