Artificial Intelligence and Deep Learning Accelerate Efforts to Develop Clean, Virtually Limitless Fusion Energy
The Fusion Recurrent Neural Network reliably forecasts disruptive and destructive events in tokamaks.
The Fusion Recurrent Neural Network reliably forecasts disruptive and destructive events in tokamaks.
First measurements of heat flux in plasmas experientially sheds light on models relying on classical thermal transport.
Scientists tame damaging edge instabilities in steady-state conditions required in a fusion reactor.
A novel experimental geometry at the Linac Coherent Light Source reveals, for the first time, how silicon responds to shocks similar to those in a planet’s core.
Spectroscopic measurements reveal that main ions flow much faster than impurities at the edge of fusion-relevant plasmas.
Surprisingly, a magnetic island does not necessarily perturb the plasma current in a dangerous way and destroy fusion performance.
Scientists discover why solar flares produce X-rays; a few electrons avoid collisions and accelerate to produce a microsecond burst.
The mechanism responsible for creating intense magnetic fields in laser-driven plasmas also helps tear the fields apart.
A new phenomena forms vortices that trap particles, impeding electron avalanches that harm fusion reactors.
U.S. and Korean scientists show how to find and use beneficial 3-D field perturbations to stabilize dangerous edge-localized modes in plasma.
Charged particles emanating from Jupiter’s magnetosphere are powered up to create the northern and southern lights on Ganymede, Jupiter’s largest moon.
In magnetic confinement fusion devices known as tokamaks, the maximum operational density limits the efficiency and now we know how this limit may be overcome.