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NSTX-U

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The National Spherical Torus Experiment (NSTX), which underwent a $94 million upgrade, is being repaired. It will be the most powerful experimental fusion facility, or tokamak, of its type in the world when it is back in operation. Experiments will test the ability of the upgraded spherical facility to maintain a high-performance plasma under conditions of extreme heat and power. Results could strongly influence the design of future fusion reactors.

Tracking major sources of energy loss in compact fusion facilities

A key obstacle to controlling on Earth the fusion that powers the sun and stars is leakage of energy and particles from plasma, the hot, charged state of matter composed of free electrons and atomic nuclei that fuels fusion reactions. At the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), physicists have been focusing on validating computer simulations that forecast energy losses caused by turbulent transport during fusion experiments.

Tracking major sources of energy loss in compact fusion facilities

A key obstacle to controlling on Earth the fusion that powers the sun and stars is leakage of energy and particles from plasma, the hot, charged state of matter composed of free electrons and atomic nuclei that fuels fusion reactions. At the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), physicists have been focusing on validating computer simulations that forecast energy losses caused by turbulent transport during fusion experiments.

Machine learning speeds modeling of experiments aimed at capturing fusion energy on Earth

Machine learning (ML), a form of artificial intelligence that recognizes faces, understands language and navigates self-driving cars, can help bring to Earth the clean fusion energy that lights the sun and stars. Researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) are using ML to create a model for rapid control of plasma — the state of matter composed of free electrons and atomic nuclei, or ions — that fuels fusion reactions.

Machine learning speeds modeling of experiments aimed at capturing fusion energy on Earth

Machine learning (ML), a form of artificial intelligence that recognizes faces, understands language and navigates self-driving cars, can help bring to Earth the clean fusion energy that lights the sun and stars. Researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) are using ML to create a model for rapid control of plasma — the state of matter composed of free electrons and atomic nuclei, or ions — that fuels fusion reactions.

Speeding the development of fusion power to create unlimited energy on Earth

Can tokamak fusion facilities, the most widely used devices for harvesting on Earth the fusion reactions that power the sun and stars, be developed more quickly to produce safe, clean, and virtually limitless energy for generating electricity? Physicist Jon Menard of the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) has examined that question in a detailed look at the concept of a compact tokamak equipped with high temperature superconducting (HTS) magnets.

Steve Cowley: The knight who leads the Lab has “the most fun job”

“It’s just all been fun, and this is the most fun job I’ve ever had,” Steve Cowley says of his much-decorated career and his new position, which he assumed July 1, as the seventh director of the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) — the place where the British-born physicist earned his doctorate and that he calls “the most important fusion laboratory in the world.”

Knighted in October 

Undergraduate students extoll benefits of national laboratory research internships in fusion and plasma science

They gathered in the lobby of the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) in dresses and suits, standing in front of posters showing computer-aided-design (CAD) drawings, mathematical equations, and line graphs, preparing to explain a summer of plasma physics research.

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