A new initiative aims to accelerate the deployment of fusion energy, and UC San Diego hopes to apply supercomputing to the cause.
The U.S. Department of Energy's Fusion Innovative Research Engine (FIRE) recently dedicated funds to General Atomics to build the Fusion Energy Data Ecosystem and Repository (FEDER). FEDER will allow multiple institutions to freely and securely share experiment data, simulation results, and workflows with one another, which should accelerate breakthroughs in fusion energy.
In addition to UC San Diego, other institutions participating in FEDER include Lawrence Livermore National Laboratory, Idaho National Laboratory, the Massachusetts Institute of Technology, Hewlett Packard Enterprise, University of California, Los Angeles, and West Virginia University.
"We already have the building blocks. Now it's time to connect them," said Ilkay Altintas, chief data science officer at San Diego Supercomputer Center. "Within the first year, FEDER will weave these tools together and offer an accessible, scalable service that will grow into a lasting national resource."
The current energy grid relies heavily on coal and gas. Fusion energy is an important next step in meeting growing energy demands while keeping atmospheric pollution low. Solar and wind power are now accessible and properly renewable, but nuclear options like fusion have a high generating potential to contribute to the mix.
Other initiatives in the UK and Canada are also making strides toward market-ready fusion energy. With the help of programs like FEDER, the Department of Energy aims to have a fusion power plant up and running sometime in the 2040s.
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"Fusion research advances fastest when data flows freely and securely rather than remain trapped in isolated silos, proprietary formats, and disconnected analysis tools," said Dr. Raffi Nazikian, director of Fusion Data Science at General Atomics. "FEDER will break down barriers between institutions and disciplines, integrating datasets, computational models and research workflows. We will be able to capture new data in FEDER's living commons and make them available for immediate reuse in the next experiment or simulation."
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