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New ML framework predicts shifts between shots at DIII-D
An artist's sketch and a cross-section view of the DIII-D tokamak. (Images: General Atomics)
At the DIII-D National Fusion Facility near San Diego, Calif., home to the largest operating tokamak in North America, researchers from Thomas Jefferson National Accelerator Facility worked to develop a machine learning framework capable of adaptively predicting changes in a tokamak’s hardware.
The team’s research was recently published in the journal Machine Learning with Applications.
Mikhail L. Shmatov, Milan Kalal
Fusion Science and Technology | Volume 61 | Number 3 | April 2012 | Pages 248-255
Technical Note | doi.org/10.13182/FST12-A13538
Articles are hosted by Taylor and Francis Online.
Measures that provide high reliability and safety of inertial fusion energy (IFE) and hybrid power plants in seismic areas are considered. These measures are related mainly to the choice of liquid materials and the optimization of the designs of drivers and thermonuclear targets. It is shown that during usual operation of IFE and hybrid power plants fast ignition scenarios with the attempts to create two hot spots in one blob of compressed fuel can be expedient.