Work

Turbulence suppression by energetic particles: a sensitivity-driven dimension-adaptive sparse grid framework for discharge optimization

Year: 2021

Type: article

Abstract: A newly developed sensitivity-driven approach is employed to study the role of energetic particles in suppressing turbulence-inducing micro-instabilities for a set of realistic JET-like cases with NBI deuterium and ICRH 3He fast ions. First, the efficiency of the sensitivity-driven approach is showcased for scans in a 21-dimensional parameter space, for which only 250 simulations are necessary. The same scan performed with traditional Cartesian grids with only two points in each of the 21 dimensions would require 221 = 2, 097, 152 simulations. Then, a 14-dimensional parameter subspace is considered, using the sensitivity-driven approach to find an approximation of the parameter-to-growth rate map averaged over nine bi-normal wave-numbers, indicating pathways towards turbulence suppression. The respective turbulent fluxes, obtained via nonlinear simulations for the optimized set of parameters, are reduced by more than two order of magnitude compared to the reference results.

Source: Nuclear Fusion

DOI: 10.1088/1741-4326/abecc8

Authors: Ionuţ-Gabriel Farcaş, Alessandro Di Siena, Frank Jenko

Institutions: The University of Texas at Austin, Max Planck Institute for Plasma Physics, Technical University of Munich

Language: English

Topic: Magnetic confinement fusion research

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Office of Science, Air Force Office of Scientific Research

Awards: G2528659553, G3886122786