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Machine Learning may help capture fusion energy on Earth

IANS  |  New York 

A team of researchers in the US have used (ML) to rapidly control that fuels fusion reactions, paving the way to help Earth get the that lights the Sun and stars.

Researchers at the U.S. Department of Energy's (DOE) Princeton Laboratory (PPPL) are using to create a model for rapid control of -- the state of matter composed of free electrons and atomic nuclei, or ions.

The Sun and most stars are giant balls of plasma that undergo constant fusion reactions. Here on Earth, scientists must heat and control the plasma to cause the particles to fuse and release their

Researchers now show can facilitate such control.

The team led by trained -- the core of software -- on data produced in the first operational campaign of the National Spherical Torus Experiment-Upgrade (NSTX-U), the flagship fusion facility at PPPL.

The trained model accurately reproduced predictions of the behaviour of the energetic particles produced by powerful neutral beam injection (NBI) that is used to NSTX-U plasmas and heat them to million-degree, fusion-relevant temperatures.

"The new ML software reduces the time needed to accurately predict the behavior of energetic particles to under 150 microseconds -- enabling the calculations to be done online during the experiment," the findings showed.

The rapid evaluations will also help operators make better-informed adjustments between experiments that are executed every 15-20 minutes during operations.

"Accelerated modeling capabilities could show operators how to adjust NBI settings to improve the next experiment," said Boyer, of a paper in the journal



(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)

First Published: Sun, May 19 2019. 15:22 IST