Researchers at MIT are employing machine learning techniques to better understand turbulent plasma phenomena in fusion devices. According to MIT News, a new deep learning framework was developed that leverages artificial neural networks to represent a reduced turbulence theory. The research is described in two papers, published in Physical Review E and Physics of Plasmas. If researchers hope to control fusion for energy production, they need a better understanding of the turbulent motion of ions and electrons in plasmas moving through fusion reactors. The field lines of toroidal structures known as tokamaks force the plasma particles; the intent is to confine them long enough to produce significant net energy gains, but that's a challenge with extraordinarily high temperatures but also small spaces.

To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel. Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT's Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces -- factors that impact fusion reactor designs. To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior.

To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel. Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT's Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces – factors that impact fusion reactor designs. To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior.

Fusion devices called tokamaks run increased risk of disruptions as researchers, aiming to maximize fusion power to create on Earth the fusion that powers the sun and stars, bump up against the operational limits of the facilities. Scientists thus must be able to boost fusion power without hitting those limits. This capability will be crucial for ITER, the large international tokamak under construction in France to demonstrate the practicality of fusion energy. Fusion reactions combine light elements in the form of plasma -- the hot, charged state of matter composed of free electrons and atomic nuclei that makes up 99 percent of the visible universe -- to generate massive amounts of energy. Scientists around the world are seeking to create fusion for a virtually inexhaustible supply of safe and clean power to generate electricity.