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University of California, Davis
Electrical and Computer Engineering
- Diagnostics
- Enabling Technologies
- Paths to Commercialization
- Modeling and Simulation
- Experimental Capabilities
Unique Fusion Expertise: Fusion Plasma Diagnostics Group at the University of California, Davis The Fusion Diagnostics Group at UC Davis brings nearly five decades of focused experience in plasma measurement and microwave technology. We maintain a strong history of collaboration with leading institutions worldwide, including EUROfusion, the Max Planck Institute for Plasma Physics, General Atomics, the Princeton Plasma Physics Laboratory, Commonwealth Fusion Systems, the National Institute for Fusion Science (Japan), and the Korea Institute of Fusion Energy. Microwave Diagnostics (Hardware Development) We have successfully independently developed a highly integrated, producible, System-on-Chip-based microwave diagnostic module characterized by high gain and low noise. Deployed on the DIII-D tokamak since 2019, this module has achieved a maintenance-free operational record spanning six years. It represents the precise technology required for future fusion reactor diagnostics, with a Technology Readiness Level exceeding TRL 7. In 2024, we successfully developed the first radiation-hardened microwave diagnostics chip, which has completed laboratory testing, reaching TRL 5. Preparations are currently underway for neutron exposure testing. Synthetic Diagnostics and AI-Assisted Feedback Control (Software & Theory) Based on the fundamental interaction of waves and particles in plasma, we collaborated with the Princeton Plasma Physics Laboratory in 2016 to develop a two-dimensional Finite-Difference Time-Domain microwave synthetic diagnostics platform under the cold plasma assumption. This platform has been applied to tokamaks including DIII-D and NSTX-U. Between 2018 and 2020, we significantly enhanced this synthetic model by incorporating a Maxwellian electron velocity distribution, weak relativistic effects, and three-dimensional particle trajectory tracking simulations. This upgraded model has been integrated into the DIII-D OMFIT framework for experimental validation. Starting in 2020, we initiated a collaboration with the artificial intelligence team at Princeton University (Professor William Tang and Michael Churchill) to explore the use of microwave diagnostic data for predicting plasma disruptions. Over the past three years, through both simulation and experimental validation, we have demonstrated that microwave diagnostics can detect smaller-scale magnetic islands at an earlier stage compared to conventional magnetic probes. This capability enables earlier detection and identification, providing critical support and valuable lead time for real-time AI-assisted disruption prediction and feedback control.
Email: [email protected]