22 citations · 47 across the 5 of their papers we have counts for
5 papers
ELMO: An Uncertainty-Aware Simulation-to-Surrogate Workflow for Fast Pedestal Linear-Stability Prediction
Nami Li, X. Q. Xu, T. Osborne +5
Rapid prediction of pedestal linear stability is important for exploring tokamak operating space, uncertainty quantification, and future model-informed control, but mode-resolved m…
Physics insights from a large-scale 2D UEDGE simulation database for detachment control in KSTAR
Menglong Zhao, Xueqiao Xu, Ben Zhu +8
A large-scale database of two-dimensional UEDGE simulations has been developed to study detachment physics in KSTAR and to support surrogate models for control applications. Nearly…
Impact of pedestal density gradient and collisionality on ELM dynamics
Nami Li, X. Q. Xu, Y. F. Wang +3
BOUT++ turbulence simulations are conducted to capture the underlying physics of the small ELM characteristics achieved by increasing separatrix density via controlling strike poin…
Data-driven model for divertor plasma detachment prediction
Ben Zhu, Menglong Zhao, Harsh Bhatia +5
We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our a…
Characteristics of grassy ELMs and its impact on the divertor heat flux width
Nami Li, X. Q. Xu, N. Yan +5
BOUT++ turbulence simulations are conducted for a 60s steady-state long pulse high \{beta}p EAST grassy ELM discharge. BOUT++ linear simulations show that the unstable mode spectru…