Researchers trained a deep reinforcement learning algorithm to adjust magnetic confinement fields in real time to maintain plasma stability.
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.
Plasmas with negative triangularity show reduced gradients that develop into instabilities, including under conditions relevant to fusion power plants.
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.
Plasma simulations, theory, and comparison with experiment show that resistive wall tearing mode can cause energy loss in tokamaks.
Scientists used tokamak plasmas to study how heat shield materials protect spacecraft in the extreme conditions of atmospheric entry.
Long predicted by theory with support from supercomputers, this combination of neutrons advances nuclear physics
