Minisymposium

The plasma-material interaction poses a common difficulty in all fusion concepts, affecting both armor material lifetimes and fusion plasma performance. With long experimental lead times, modeling and simulation are critical drivers for advancement. This work presents a framework and specific applications to accurately quantify the impacts of plasma sheath and material sputtering effects on materials. The software stack builds useful abstractions on top of exascale computing project-funded tools like Kokkos and Cabana to develop applications. The results of this work are presented for two target areas: the plasma sheath, including sputtering and feedback to the sheath, and the energy transfer of resonant ions in the ion cyclotron heating (ICH) region to non-resonant species. Results show prompt redeposition fractions and ion energy distributions of atoms that have returned to the surface. Temperature equilibration rates are shown for the ICH-heated edge plasma.

This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy.This research was supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy

The fusion device optimization will require accurate reduced order models to explore design space and identify a conceptual design. Existing system codes used for fusion applications rely on scaling laws that do not have desired accuracy. There is a need for knowledge transfer from high-fidelity to low-fidelity models producing computationally efficient and accurate reduced order representation. The AI/ML approaches are a perfect candidate for this task. One of the specific challenges of the blanket design in magnetic confinement fusion are MHD effects present in the coolant and/or breeder. The MHD effects have significant influence on pressure drop and heat transfer both being extremely influential in system design. These effects are also difficult to model often requiring direct numerical simulations and fine mesh resolution to capture steep wall gradients. Fortunately, this analysis can be replaced with lower-fidelity approaches such as RANS with appropriate turbulence models that capture MHD effects on the mean flow. However, the 3D turbulence models for MHD flows are not available. As part of the VERTEX project, we have developed an AI/ML model with LES database that can capture the influence of the MHD effects on turbulent flow showcasing a successful knowledge transfer from high-fidelity to low-fidelity models.

In a fusion reactor sub-system called blanket, a liquid breeder (liquid metal LM or molten salt MS) flows for tritium breeding and conversion of plasma energy into heat. LM flows in the fusion blanket are strongly affected by the plasma-confining magnetic field and volumetric heating that drastically change the flow behavior and modify associated transport processes. Predicting magnetohydrodynamic (MHD) flows, corrosion, and tritium transport is critically important to any LM blanket design. The computational magnetohydrodynamic (CMHD) tools have been successfully used by fusion researchers to assist in the blanket design and analysis since the first computations in 1970’s. However, for decades, the applicability of these tools was limited to relatively simple geometries, low flow velocities and low to moderate magnetic fields. This paper overviews recent progress and future directions in the CMHD area for fusion with the main focus on the existing and new CMHD codes, numerical methods, and applications. Two examples presented in the paper, high magnetic field lead-lithium blanket, and integrated computer modelling for the Dual Coolant Lead Lithium (DCLL) blanket, evidence that the CMHD codes are reaching the stage where they can be used as a real design tool.

In this open discussion, the obstacles that must be overcome to effectively incorporate the relevant physics for fusion energy devices will be considered. This round table discussion will explore the topics of the accompanying talks and will explore the avenues for funding, timelines, and major hurtles to accomplishing the goal of high-fidelity simulations. Participants will be encouraged to share ideas on techniques for bridging the gap between high-fidelity simulations and engineering-scale models. The three main topics of discussion will be:

(1) What engineering challenges does fusion energy face while working toward fusion reactor deployment? (2) What tools are needed to solve those engineering challenges? (3) How can AI and machine learning be leveraged to bridge the gap between high-fidelity modeling and engineering-scale analysis?