Monday, August 22, 2022
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 and maintain high steady-state pressures in the core of the reactor to reach self-sustaining fusion conditions. Since turbulence is the main source of heat transport and losses, there is an opportunity for improving reactor performance by optimizing the design for turbulent transport. In this talk, I will present a vision for tackling this challenging problem in a scalable way, consisting of three main modules: (1) fast-but-accurate turbulence modeling with GX, a GPU-native gyrokinetic code that uses pseudo-spectral (Fourier-Hermite-Laguerre) methods; (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; and (3) transport optimization of fusion reactor designs by using (1-2) as a transport model inside the optimization loop.
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