Engineering topics

Nelson, Andrew O.

Publications and source records attributed to Nelson, Andrew O..

2 records

DOE OSTI2023

Understanding the negative triangularity ELM trigger and ELM free state on DIII-D with ECE-imaging

Here, the Electron Cyclotron Emission Imaging (ECEI) diagnostic was used to observe a finite-n interchange mode structure in the edge of negative triangularity shaped plasmas on DIII-D. At a small negative triangularity (δ u = -0.2), the plasma is in the H-mode with ELMs that are triggered by a low-n interchange mode. At a larger negative triangularity (δ u = -0.4) and low NBI power (2 MW), a dithering oscillation is observed that is triggered by a low-n interchange mode, whereas at higher NBI power (>2 MW), the edge reverts to L-mode and the low-n interchange mode is present continuously. In all cases, the edge pressure gradient is clamped when the interchange mode is present. It is concluded that the low-n interchange mode prevents the plasma from transitioning to H-mode at a large negative triangularity. This agrees with linear BOUT++ simulations which suggest that the interchange-type MHD can be a resistive ballooning mode whereby resistivity can significantly increase the finite-n ballooning mode growth rate. The absence of H-mode at large negative triangularity can, thus, be explained by the excitation of low-n pressure driven resistive ballooning modes in the plasma edge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. However, despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗