Controller with Integrated Machine Learning Tweaks Fusion Plasmas in Real Time
Integrating machine learning with real-time adaptive control produces high-performance plasmas without edge instabilities, a key for future fusion reactors.
Integrating machine learning with real-time adaptive control produces high-performance plasmas without edge instabilities, a key for future fusion reactors.
Study finds that neutral beam performance can be experimentally deduced from electron temperature evolution during neutral beam injection.
The first measurement of ion temperature in magnetic islands identified a steep gradient, providing insights for improving plasma confinement in tokamaks.
By achieving very high density and confinement quality at the same time, researchers make new strides toward fusion energy.
Settling a long-standing question, scientists have proven that antihydrogen falls downward in a first-ever direct experiment.
Plasmas with negative triangularity show reduced gradients that develop into instabilities, including under conditions relevant to fusion power plants.
Neural networks guided by physics are creating new ways to observe the complexities of plasmas.
Perturbing the edge magnetic field of a tokamak produces a counterintuitive response: particles entering the confined region rather than escaping it.
For the first time, scientists successfully track energetic ion flow through space and energy driven by electromagnetic waves in fusion plasmas.
Small rotating magnetic islands in tokamaks flowing at the same speed can couple together to cause disruptive islands that reduce plasma confinement.
Computation and simulations show that different types of collisions compete to determine the way energy is transferred between particles and plasma waves.
Scientists use supercomputer simulations to understand the complex interplay between large-scale ion and small-scale electron plasma motion in determining fusion performance