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

被引：4

作者：

Farcas, Ionut-Gabriel ^{[1
]}

Di Siena, Alessandro ^{[1
]}

Jenko, Frank ^{[1
,2
,3
]}

机构：

[1] Univ Texas Austin, Oden Inst Computat Engn & Sci, Austin, TX 78712 USA

[2] Max Planck Inst Plasma Phys, D-85748 Garching, Germany

[3] Tech Univ Munich, D-85748 Garching, Germany

来源：

NUCLEAR FUSION | 2021年 / 61卷 / 05期

关键词：

turbulence suppression; energetic particles; sensitivity analysis; adaptive sparse grids; optimization;

D O I：

10.1088/1741-4326/abecc8

中图分类号：

O35 [流体力学]; O53 [等离子体物理学];

学科分类号：

070204 ; 080103 ; 080704 ;

摘要：

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.

引用

页数：10