Engineering topics

Fredrickson, Eric D.

Publications and source records attributed to Fredrickson, Eric D..

3 records

DOE OSTI2022

Nonlinear growth of magnetic islands by passing fast ions in NSTX

The growth of magnetic islands in NSTX is modeled successfully, with the consideration of passing fast ions. It is shown that a good quantitative agreement between simulation and experimental measurement can be achieved when the uncompensated cross-field current induced by passing fast ions is included in the island growth model. The fast ion parameters, along with other equilibrium parameters, are obtained self-consistently using the TRANSP code with the assumptions of the 'kick' model (Podestà et al 2017 Plasma Phys. Control. Fusion 59 095008). Overall, the results show that fast ions can contribute to overcoming the stabilizing effect of polarization current for magnetic island growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Compressional Alfvén eigenmodes excited by runaway electrons

Compressional Alfvén eigenmodes (CAEs) driven by energetic ions have been observed in magnetic fusion experiments. In this paper, we show that the modes can also be driven by runaway electrons formed in post-disruption plasma, which may explain kinetic instabilities observed in DIII-D disruption experiments with massive gas injection. The spatial structure is calculated, as are the frequencies which are in agreement with experimental observations. Using a runaway electron distribution function obtained from a kinetic simulation, the mode growth rates are calculated and found to exceed the collisional damping rate when the runaway electron density exceeds a threshold value. The excitation of CAEs poses a new possible approach to mitigate seed runaway electrons during the current quench and surpassing the avalanche.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX

Abrupt large events in the Alfvenic and sub-Alfvenic frequency bands in tokamaks are typically correlated with increased fast ion loss. Here, machine learning is used to speed up the laborious process of characterizing the behaviour of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans. Analysis allows for comparison between different mode character (such as quiescent, fixedfrequency, chirping, avalanching) and plasma parameters obtained from the TRANSP code such as the ratio of the neutral beam injection (NBI) velocity and the Alfven velocity (v inj. /v A ), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (β beam,i /β). In agreement with previous work by Fredrickson et al., in this work, we find correlation between β beam,i and mode character. In addition, previously unknown correlations are found between moments of the spectrograms and mode character. Character transition from quiescent to non-quiescent behaviour for magnetic fluctuations in the 50 - 200 kHz frequency band is observed along the boundary v Φ ≲ (1/4) (v inj. - 3v A ) where v Φ is the rotation

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗