Source: DOE Science News | Release | July , 2019

New deep learning code predicts destructive plasma instabilities in record time

Using neural networks, the FRNN code identifies patterns in plasma behavior to quickly and accurately predict disruptions in fusion reactors.

Credit: Eliot Feibush, Princeton Plasma Physics Laboratory, and Julian Kates-Harbeck, Harvard University. Using neural networks, the FRNN code identifies patterns in plasma behavior to quickly and accurately predict disruptions in fusion reactors.

For decades, scientists have sought to control nuclear fusion—the energy that powers the sun and other stars—by developing massive fusion reactors to produce and contain plasma, with the goal of mirroring the astronomically high pressure and temperature conditions of celestial objects.

To ensure plasma—the fourth fundamental state of matter—retains its heat and does not interact with materials in the containment vessel, researchers employ doughnut-shaped fusion devices called tokamaks, which use magnetic fields to trap fusion reactions in place. However, large-scale plasma instabilities called disruptions can interfere with this process.

During disruptions, plasma rapidly escapes from the confined area and reaches the walls of the tokamak. In addition to stopping the reaction, this contact transfers intense levels of heat, which can cause serious or irreparable damage to the reactor.

A team of researchers led by Bill Tang of the US Department of Energy’s (DOE’s) Princeton Plasma Physics Laboratory (PPPL) and Princeton University recently tested its Fusion Recurrent Neural Network (FRNN) code on various high-performance computing (HPC) systems, including the 27-petaflop Titan and the 200-petaflop Summit, the world’s most powerful and smartest supercomputer for open science.

“We aim to accurately predict the potential for disruptive events before they occur, as well as understand the reasons why they happen in the first place,” Tang said.

Both Titan and Summit are located at the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility at DOE’s Oak Ridge National Laboratory. The team’s results are published in Nature.

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