A5104151272- Magnetic confinement fusion research
- Superconducting Materials and Applications
- Ionosphere and magnetosphere dynamics
- Particle accelerators and beam dynamics
- Fusion materials and technologies
- Laser-Plasma Interactions and Diagnostics
Seoul National University
2023-2024
Abstract The edge region of the recently discovered fast ion-regulated enhancement mode (FIRE mode) (Han and Park et al 2022 Nature 609 269–275), which primarily operates in unfavorable ion <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="normal">∇</mml:mi> <mml:mi>B</mml:mi> </mml:mrow> </mml:math> drift configuration, is investigated detail for first time. An temperature pedestal identified, while electron density profile remains...
Abstract The neural network model, MISHKA-NN is developed to mitigate the computational burden associated with linear ideal magnetohydrodynamic (MHD) stability analysis of pedestal based on peeling–ballooning (P–B) model. By utilizing both 1D plasma profiles (current density, pressure gradient, and safety factor) 0D parameters (plasma geometry, total current, toroidal mode number), model predicts growth rate edge-localized MHD instability in a given equilibrium state. enabling prediction...
The new neural-network model for the pedestal linear MHD (magnetohydrodynamic) stability analysis is developed, to accelerate speed and reduce numerical burden. This predicts growth rates of edge-localized instabilities KSTAR-like (Korea Superconducting Tokamak Advanced Research) plasma with a structure at edge. trained by data set consisting parametric equilibria calculation results. It has successfully predicted most unstable toroidal mode number benchmark cases within reasonable errors....