Plasma Physics / Fusion Colloquium with Dr. Darin Ernst, MIT

Speaker: Dr. Darin Ernst, MIT

Title: "Developing Viable Operating Regimes for Fusion Pilot Plants and Predicting their Performance"

Abstract: As the magnetic fusion program moves toward pilot plants, we must change the way we operate. Major challenges must be overcome to avoid machine damage, wall erosion, and impurity contamination while maintaining high temperatures. High boundary temperatures sputter metallic impurities, which are drawn into the plasma and degrade performance by radiation. Large edge localized mode (ELM) transients effectively flush these metallic impurities, but would damage the divertor and are thus intolerable. Fortunately, scaling arguments suggest that in future machines, turbulent transport is more likely to create naturally ELM-stable scenarios, with additional benefits.1 However, new challenges arise, such as avoiding metallic impurity accumulation. I will describe our efforts to meet these core-edge integration challenges in future-relevant ELM-stable scenarios such as Quiescent H-Mode in DIII-D. For example, we recently achieved record densities via strong shaping, which enabled the first radiative divertor plasma detachment in QH-mode, while reducing impurity influxes by a factor of six by tailoring edge pedestal profiles.
    Fusion performance is highly sensitive to the edge pressure. For ELM-free regimes, the edge pressure is limited by turbulence rather than MHD stability (which is readily predicted by the EPED model). This exposes a major gap in predictive capabilities. We are addressing this grand challenge in the 11-institution DOE Fusion Innovation Research Engine (FIRE) Collaboratory, APP-FPP: Advanced Profile Prediction for Fusion Pilot Plant Design. Our project is developing accelerated gyrokinetic, multiscale, whole-device predictions of density, temperature & impurity profiles for tokamak and stellarator fusion pilot plants, including kinetic plasma-wall interactions and atomic physics. Making these predictions practical requires highly accelerated algorithms and AI/ML techniques. The present focus is on extending, accelerating, and coupling edge gyrokinetic codes and widely used fluid and gyrokinetic transport solvers to predict edge profiles. Reduced models also enter for the core and edge, such as our new MIT multiscale gyrofluid code, which can accurately simulate coupled ion and electron scale turbulence on a GPU in 2-3 hours, several orders of magnitude faster than gyrokinetic codes, while matching their results.

¹ D. R. Ernst et al., Broadening of the divertor heat flux profile in high confinement tokamak fusion plasmas with edge pedestals limited by turbulence in DIII-D, Physical Review Letters 132, 235102 (2024).

Bio: Ernst has been a member of the Theory Group at the MIT Plasma Science and Fusion Center since 2002, following a postdoc at Princeton Plasma Physics Laboratory. He received his B.S. from the University of Wisconsin, completing majors in Electrical Engineering, Physics, and Math. As an MIT graduate student, he played a significant role in the TFTR D-T campaign. This inspired a model explaining Supershot confinement scalings and rotation profiles which received the APS Rosenbluth Award for Outstanding Doctoral Thesis. Since 2007 Ernst been the MIT Principal Investigator for gyrokinetic SciDAC projects, supervising theory postdocs and students in the development of gyrokinetic, gyro-fluid, and neoclassical codes, and now leads the APP-FPP FIRE Collaboratory. He has been a long-time collaborator on the DIII-D National Fusion Facility, leading an experiment every year on average. Active in community roles, Ernst led the 2022 DOE Joint Research Target and the 2022-2023 DIII-D Thrust to Develop High Performance Non-ELMing Regimes, and now leads similar international efforts in ITPEA, as well as the QH-Mode subgroup of the EU-US Joint Working Group on ELM-Free Regimes. He has presented 32 invited talks at international conferences and is author/co-author of over 100 refereed articles. https://sites.mit.edu/darinernst  
 

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