For the first time, scientists successfully track energetic ion flow through space and energy driven by electromagnetic waves in fusion plasmas.
Perturbing the edge magnetic field of a tokamak produces a counterintuitive response: particles entering the confined region rather than escaping it.
Neural networks guided by physics are creating new ways to observe the complexities of plasmas.
Computation and simulations show that different types of collisions compete to determine the way energy is transferred between particles and plasma waves.
For the first time, the error correction process significantly enhances the lifetime of quantum information.
Scientists use supercomputer simulations to understand the complex interplay between large-scale ion and small-scale electron plasma motion in determining fusion performance
National laboratory researchers partner with a private company to achieve 100-million-degree temperatures inside a high magnetic field spherical tokamak.
Machine learning techniques track turbulent blobs in millions of frames of video from tokamak experiments.
Plasma simulations, theory, and comparison with experiment show that resistive wall tearing mode can cause energy loss in tokamaks.
Roughening of fusion reactor wall surfaces over time may significantly reduce erosion rate predictions