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General Atomics Deploys Machine Learning to Optimize DOE“™s Nuclear Fusion Reactor

General Atomics Deploys Machine Learning to Optimize DOE“™s Nuclear Fusion Reactor - top government contractors - best government contracting event
General Atomics Deploys Machine Learning to Optimize DOE's Nuclear Fusion Reactor

General Atomics Deploys Machine Learning to Optimize DOE“™s Nuclear Fusion Reactor - top government contractors - best government contracting event

General Atomics researchers have developed a machine learning-based approach to measuring the nuclear fusion uncertainty levels of the DIII-D tokamak device in San Diego.

General Atomics said Monday it used a neural network to allow plasma to operate near its vertical stability threshold to optimize performance while preventing nuclear fusion disruptions.

The researchers leveraged the company“™s TokSearch data mining technology to train the neural network using large amounts of data from DIII-D experimental activities.

According to the company, its real-time fusion controls will also support the development of future reactors intended to operate for extended periods of time.

“We“™re now starting to be able to use modern machine-learning techniques to augment our physics understanding, and this allows us to control the plasma more effectively,“ said Brian Sammuli, one of the researchers for the effort.

“Although DIII-D has applied machine learning to real-time prediction of instabilities for decades, actual real-time control to prevent disruption using these massive data sets is very novel and exciting,“ noted David Hill, director of DIII-D.

The tokamak, a device that turns fuel into fusion energy, housed at the DIII-D National Fusion Facility is under the operation of General Atomics on behalf of the Department of Energy“™s Office of Science.

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