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Nonlinear gyrokinetic predictions of SPARC burning plasma profiles enabled by surrogate modeling

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Nonlinear gyrokinetic predictions of SPARC burning plasma profiles enabled by surrogate modeling
 
Identifier https://doi.org/10.7910/DVN/2BKB3O
 
Creator Pablo Rodriguez-Fernandez, Nathan T Howard, Jeff Candy
 
Publisher Harvard Dataverse
 
Description Multi-channel, nonlinear predictions of core temperature and density profiles are performed for the SPARC tokamak accounting for both kinetic neoclassical and fully nonlinear gyro-kinetic turbulent fluxes. A series of flux-tube, nonlinear, electromagnetic simulations using the CGYRO code with six gyrokinetic species are coupled to a nonlinear optimizer using Gaussian Process regression techniques. The simultaneous evolution of energy sources, including alpha heat, radiation, and energy exchange, coupled with these high fidelity models and techniques, leads to a converged solution in electron temperature, ion temperature and electron density channels with a minimal number of expensive gyrokinetic simulations without compromising accuracy
 
Subject Physics
Bayesian
gyrokinetics
multi-scale
Optimization
SPARC