activity
20202026
most citedAn adaptive scalable fully implicit algorithm based on stabilized finite element for reduced visco-resistive MHD

12 citations · 13 across the 8 of their papers we have counts for

collaborators

9 papers

math.NA2026

An Efficient Solver for Finite Element-based Constrained Transport in 3D Magnetohydrodynamics Applied to Magnetic Confinement Fusion

Golo A. Wimmer, Konstantin Lipnikov, Ben S. Southworth +1

We present an efficient solver framework for the stiff magnetic wave coupling arising in resistive magnetohydrodynamics (MHD) on realistic tokamak geometries. The approach builds o…

math.NA2024★ 1 cited

An adaptive Newton-based free-boundary Grad-Shafranov solver

Daniel A. Serino, Qi Tang, Xian-Zhu Tang +2

Equilibria in magnetic confinement devices result from force balancing between the Lorentz force and the plasma pressure gradient. In an axisymmetric configuration like a tokamak,…

math.NA2023

Denoising Particle-In-Cell Data via Smoothness-Increasing Accuracy-Conserving Filters with Application to Bohm Speed Computation

Matthew J. Picklo, Qi Tang, Yanzeng Zhang +2

The simulation of plasma physics is computationally expensive because the underlying physical system is of high dimensions, requiring three spatial dimensions and three velocity di…

physics.plasm-ph2023

A mimetic finite difference based quasi-static magnetohydrodynamic solver for force-free plasmas in tokamak disruptions

Zakariae Jorti, Qi Tang, Konstantin Lipnikov +1

Force-free plasmas are a good approximation where the plasma pressure is tiny compared with the magnetic pressure, which is the case during the cold vertical displacement event (VD…

math.NA2023

Scalable Implicit Solvers with Dynamic Mesh Adaptation for a Relativistic Drift-Kinetic Fokker-Planck-Boltzmann Model

Johann Rudi, Max Heldman, Emil M. Constantinescu +2

In this work we consider a relativistic drift-kinetic model for runaway electrons along with a Fokker-Planck operator for small-angle Coulomb collisions, a radiation damping operat…

physics.plasm-ph2021

Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling

Nathan A. Garland, Romit Maulik, Qi Tang +2

Reliable plasma transport modeling for magnetic confinement fusion depends on accurately resolving the ion charge state distribution and radiative power losses of the plasma. These…