Core to Edge Tokamak Impurity Transport Modeling Using Surrogate-Based Optimization

POSTER

Abstract

Impurity transport and accumulation are expected to play a major role in determining performance in the next generation of burning plasmas. As such, there is a growing need for integrated models of impurity transport, that can simultaneously predict the impact of impurities on both the core and edge/divertor regions. . This work seeks to fill this need by developing a modeling framework for impurity transport spanning the entire plasma. In this study, core impurity transport is modeled utilizing the OMFIT ImpRad module [Sciortino NF 2020] while transport in the SOL and divertor is modeled using SOLPS-ITER [Schneider CPP 2006]. The SOLPS-ITER modeling was facilitated by a newly implemented, surrogate-based optimization workflow based on the PORTALS framework [Rodriguez-Fernandez NF 2024]. This workflow allows for relatively rapid iteration through simulation transport parameters to match experimental conditions and will ultimately enable efficient iteration between core and edge models, creating a coupled impurity transport modeling framework. Preliminary results of this work applied to H-mode discharges, details of the core modeling and surrogate modeling techniques, and future plans for this work will be presented.

Presenters

  • Ivan James Marshall

    Massachusetts Institute of Technology

Authors

  • Ivan James Marshall

    Massachusetts Institute of Technology

  • Nathan T Howard

    MIT PSFC, MIT, Massachusetts Institute of Technology MIT, MIT Plasma Science and Fusion Center, Massachusetts Institute of Technology

  • Rebecca L Masline

    MIT Plasma Science and Fusion Center, Massachusetts Institute of Technology

  • Marco Andrés Miller

    MIT Plasma Science and Fusion Center, MIT PSFC

  • Leonardo Corsaro

    MIT Plasma Science and Fusion Center

  • Michael Robert Knox Wigram

    MIT Plasma Science and Fusion Center

  • Pablo Rodriguez-Fernandez

    MIT Plasma Science and Fusion Center, MIT PSFC