skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A Reduced Resistive Wall Mode Kinetic Stability Model for Disruption Forecasting

Dataset ·
DOI:https://doi.org/10.11578/1367870· OSTI ID:1367870

Kinetic modification of ideal stability theory from stabilizing resonances of mode-particle interaction has had success in explaining resistive wall mode (RWM) stability limits in tokamaks. With the goal of real-time stability forecasting, a reduced kinetic stability model has been implemented in the new Disruption Event Characterization and Forecasting (DECAF) code, which has been written to analyze disruptions in tokamaks. The reduced model incorporates parameterized models for ideal limits on beta, a ratio of plasma pressure to magnetic pressure, which are shown to be in good agreement with DCON code calculations. Increased beta between these ideal limits causes a shift in the unstable region of delta W_K space, where delta W_K is the change in potential energy due to kinetic effects that is solved for by the reduced model, such that it is possible for plasmas to be unstable at intermediate beta but stable at higher beta. Gaussian functions for delta W_K are defined as functions of E cross B frequency and collisionality, with parameters reflecting the experience of the National Spherical Torus Experiment (NSTX). The reduced model was tested on a database of discharges from NSTX and experimentally stable and unstable discharges were separated noticeably on a stability map in E cross B frequency, collisionality space. The reduced model only failed to predict an unstable RWM in 15.6% of cases with an experimentally unstable RWM and performed well on predicting stability for experimentally stable discharges as well.

Research Organization:
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Fusion Energy Sciences (FES)
DOE Contract Number:
AC02-09CH11466
OSTI ID:
1367870
Resource Relation:
Related Information: Physics of Plasmas Vol. 24 p. 056103 (May 2017)
Country of Publication:
United States
Language:
English

References (1)


Cited By (1)


Similar Records

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas
Journal Article · Wed Mar 18 00:00:00 EDT 2020 · Nuclear Fusion · OSTI ID:1367870

Disruption event characterization and forecasting in tokamaks
Journal Article · Fri Mar 17 00:00:00 EDT 2023 · Physics of Plasmas · OSTI ID:1367870

Analysis of MHD stability and active mode control on KSTAR for high confinement, disruption-free plasma
Journal Article · Fri Apr 10 00:00:00 EDT 2020 · Nuclear Fusion · OSTI ID:1367870