As we approach the breakeven era of fusion, optimizing reactors to make them more efficient and less expensive will be critical to the wide-scale adoption of fusion as a commercial energy source. The main challenge is to achieve high steady-state pressures in the core of the reactor to reach self-sustaining fusion conditions. At the same time, the boundary plasma must be kept sufficiently cool so that the plasma exhausted from the hot core is not dangerous to the device walls. Turbulence is the main source of heat transport from the core to the boundary, which makes understanding how to optimize the reactor design for turbulent transport a key to solving the competing (but coupled) core and boundary challenges. Whole-device turbulent transport modeling and optimization are areas where high-performance computing at exascale can make a large impact on the success of fusion by enabling the design of smaller, cheaper, and more efficient fusion reactors.
In this talk, I will present a vision for tackling this challenging whole-device-modeling problem in a scalable way. My approach consists of four main modules: (1) fast-but-accurate core turbulence modeling with the GPU-native GX gyrokinetic code; (2) multi-scale modeling for predicting core profiles using a macro-scale transport solver (Trinity) coupled to many GX micro-turbulence calculations in parallel, leveraging the scale separation between turbulence and transport; (3) solving the heat exhaust challenge via kinetic boundary turbulence modeling with the Gkeyll code, a full-f electromagnetic gyrokinetic model for the edge and scrape-off layer; and (4) transport optimization of fusion reactor designs at exascale by using (1-3) as a massively-parallel whole-device model.
