44 citations · 139 across the 10 of their papers we have counts for
10 papers
Unraveling Diffusion in Fusion Plasma: A Case Study of In Situ Processing and Particle Sorting
Junmin Gu, Paul Lin, Kesheng Wu +6
This work starts an in situ processing capability to study a certain diffusion process in magnetic confinement fusion. This diffusion process involves plasma particles that are lik…
Effects of collisional ion orbit loss on tokamak radial electric field and toroidal rotation in an L-mode plasma
Hongxuan Zhu, T. Stoltzfus-Dueck, R. Hager +2
Ion orbit loss has been used to model the formation of a strong negative radial electric field in the tokamak edge, as well as edge momentum transport and toroidal rotation.…
Transport Barrier and Spinning Blob Dynamics in the Tokamak Edge
Junyi Cheng, James Myra, Seung-Hoe Ku +3
In this work, we investigate the dynamics of plasma blobs in the edge of magnetic confinement devices using a full-f gyrokinetic particle-in-cell code with X-point geometry. In sim…
Enabling particle applications for exascale computing platforms
Susan M Mniszewski, James Belak, Jean-Luc Fattebert +21
The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applic…
Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning
C. S. Chang, S. Ku, R. Hager +6
Understanding and predicting divertor heat-load width is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predic…
Geometric Electrostatic Particle-In-Cell Algorithm on Unstructured Meshes
Zhenyu Wang, Hong Qin, Benjamin Sturdevant +1
We present a geometric Particle-in-Cell (PIC) algorithm on two-dimensional (2D) unstructured meshes for studying electrostatic perturbations in magnetized plasmas. In this method,…
Training neural networks under physical constraints using a stochastic augmented Lagrangian approach
Alp Dener, Marco Andres Miller, Randy Michael Churchill +2
We investigate the physics-constrained training of an encoder-decoder neural network for approximating the Fokker-Planck-Landau collision operator in the 5-dimensional kinetic fusi…
Encoder-decoder neural network for solving the nonlinear Fokker-Planck-Landau collision operator in XGC
M. A. Miller, R. M. Churchill, A. Dener +3
An encoder-decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker-Planck-Landau collision operator.…
A tight-coupling scheme sharing minimum information across a spatial interface between gyrokinetic turbulence codes
Julien Dominski, Seung-Hoe Ku, Choong-Seock Chang +5
A new scheme that tightly couples kinetic turbulence codes across a spatial interface is introduced. This scheme evolves from considerations of competing strategies and down-select…
Gyroaveraging operations using adaptive matrix operators
Julien Dominski, Seung-Hoe Ku, Choong-Seock Chang
A new adaptive scheme to be used in Particle-In-Cell codes for carrying out gyroaveraging operations with matrices is presented. This new scheme uses an intermediate velocity grid…