Improving fusion plasma codes and demonstrating the ability to accurately model plasma behavior in experiments. Research led by UC Irvine at the DIII-D National Fusion Facility.
Research
Machine learning is used to identify plasma instabilities in fusion energy reactors. Ph.D. thesis research performed at the DIII-D National Fusion Facility.
Learn about all the ways the DIII-D National Fusion Facility will contribute to the development of fusion energy over the next many years. Part of a special collection in Physics of Plasmas, “Private Fusion Research: Opportunities and Challenges in Plasma Science.”
Graduate students at the DIII-D National Fusion Facility have built a large language model (LLM) from the entire archive of experiment journal notes. The resulting interface provides remarkably good answers to questions about how to improve fusion performance in experiments.
Passive Coil To Mitigate Relativistic Electrons In Tokamaks
In many ways, a fusion reactor is passively safe; most off-normal events within such a device produce a naturally occurring behavior that leads to the calm shutdown of the fusion reaction without operator intervention. One outlier off-normal event is the formation of a relativistic electron beam, which is a very high-energy electron beam capable of […]
Tungsten Escaping a Closed Tokamak Divertor
Materials engineering for fusion energy frequently matures along two paths simultaneously. High-heat flux facilities provide qualifications that the candidate material can survive the power it will receive, and tokamaks demonstrate the feasibility of that material in the fusion plasma environment. In newly published work from S.H. Messer and colleagues, the fusion plasma exhaust in DIII-D […]