Two winning research teams will each receive $4 million over three years. According to the university, the teams are composed of UC faculty across a wide range of disciplines, representing five UC campuses and the UC-managed Lawrence Livermore and Los Alamos National Laboratories.
The funding was drawn from fee income the university receives for managing the two Department of Energy laboratories.
The two winning projects are detailed below.
- Center for Fusion Energy–Materials and Diagnostics for Extreme Conditions (MDeC)
Lead investigator: Farhat Beg, professor of mechanical and aerospace engineering and director of the Center for Energy Research, UC San Diego
Partners: LLNL, LANL, UC Irvine, UCLA, UC San Diego, UC Santa Cruz
The Center for Fusion Energy–MDeC will leverage the specialized manufacturing, measuring, and computational tools of LLNL and LANL and participating UC campuses to advance fusion materials discovery.
Faculty, staff scientists, and students will model and develop new materials that can withstand the harsh radiation environment relevant to nuclear fusion reactors. They will improve materials-tritium interactions to reduce losses. They’ll also develop new sensor materials for use in fusion diagnostics and deploy AI and machine learning to analyze data in real time.
According to UC San Diego, in addition to these research goals, a key aim of the MDeC center is to address critical fusion workforce needs by providing students at the undergraduate and graduate levels across the UC system with access to fusion energy research projects and skill sets.
- Predictive Discovery of Radiation–Resistant Alloys for Extreme Fusion Environments
Lead investigator: Penghui Cao, associate professor of mechanical and aerospace engineering, UC Irvine
Partners: LANL, LLNL, UC Irvine, UC San Diego, UC Santa Barbara, UC Berkeley
This project will establish a hub for predictive discovery and accelerated demonstration of durable, supply-resilient alloys for fusion reactors, through innovative, multiscale modeling, machine learning/AI, advanced irradiation, and performance testing. Through this research, the team will design materials property maps, providing a platform for selecting, testing, and validating materials more quickly and efficiently.
