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The SPARC Error Field Strategy

ORAL

Abstract

SPARC is a high-field DT tokamak experiment with a mission to produce twice the power absorbed by the plasma, but without care, error fields could reduce confinement or even disrupt the plasma. The SPARC error field risk is quantified by considering the probability density functions (PDF) for locking and for the intrinsic error. For the locking PDF, we choose the most sensitive SPARC scenario (i.e. the 12.2 T L-mode) and Monte Carlo sample the ITPA overlap power-law scaling [Logan 2021 PPCF]. Next, the machine intrinsic error PDF is modeled using detailed coil windings and Monte Carlo simulations of the random tilts and shifts. The magnitude of each tilt and shift is represented by a hollow distribution, giving a higher probability of being near the alignment tolerance than below it. The two PDFs are used to derive the probability that the intrinsic error exceeds the correction limit of the error field correction coils, assumed to be twice the locking threshold. Under these assumptions we prescribe the tolerances to reduce the locked mode risk to ≤0.1%, and preliminary tolerancing schemes suggest ~3 mm tolerances on most degrees of freedom. Metrology and direct identification plasma experiments will be used to measure the error field.

Presenters

  • Ryan M Sweeney

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

Authors

  • Ryan M Sweeney

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

  • Nikolas C Logan

    Lawrence Livermore Natl Lab, LLNL

  • Clayton E Myers

    Commonwealth Fusion Systems, CFS, Sandia National Laboratories

  • Carlos A Paz-Soldan

    Columbia University

  • Alexander J Creely

    Commonwealth Fusion Systems, CFS

  • Robert S Granetz

    Massachusetts Institute of Technology (MIT), Massachusetts Institute of Technology MI, Massachusetts Institute of Technology, MIT Plasma Science and Fusion Center, MIT

  • Cristina Rea

    Massachusetts Institute of Technology MI

  • Ruben Tukker

    Massachusetts Institute of Technology MIT

  • Ted Wyeth

    Commonwealth Fusion Systems

  • Zimi Zhang

    Massachusetts Institute of Technology MIT