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

TL;DR

This paper introduces a sensitivity-driven sparse grid framework to efficiently optimize plasma discharge parameters, demonstrating significant turbulence suppression with far fewer simulations than traditional methods.

Contribution

The paper presents a novel sensitivity-driven sparse grid approach for high-dimensional parameter scans in plasma physics, drastically reducing computational cost while optimizing turbulence suppression.

Findings

Efficient 21-dimensional parameter scan with only 250 simulations.

Significant turbulence reduction by over two orders of magnitude.

Identification of parameter pathways for turbulence suppression.

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 $^3$He 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.