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

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 He-3 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 2(21) = 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.

Automated summary

The newly developed sensitivity-driven approach effectively studies the role of energetic particles in suppressing turbulence-inducing micro-instabilities in realistic JET-like cases, showcasing significant efficiency gains in computational resources. By scanning a parameter subspace using the sensitivity-driven approach, pathways towards turbulence suppression were found through an approximation of the parameter-to-growth rate map.