The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
It would be a challenge for any scientist to match Alexey Serov’s rate of inventions related to green hydrogen fuel. But this researcher at ORNL has 84 patents with at least 35 more under review, so his electrifying pace is unlikely to slow down any time soon.
Ateios Systems licensed an ORNL technology for solvent-free battery component production using electron curing. Through Innovation Crossroads, Ateios continues to work with ORNL to enable readiness for production-quality battery components.
ORNL will lead a new DOE-funded project designed to accelerate bringing fusion energy to the grid. The Accelerate award focuses on developing a fusion power plant design concept that supports remote maintenance and repair methods for the plasma-facing components in fusion power plants.
Two fusion energy leaders have joined ORNL in the Fusion and Fission Energy and Science Directorate, or FFESD.
A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.
Nuclear engineering students from the United States Military Academy and United States Naval Academy are working with researchers at ORNL to complete design concepts for a nuclear propulsion rocket to go to space in 2027 as part of the Defense Advanced Research Projects Agency DRACO program.
A team of eight scientists won the Association for Computing Machinery’s 2023 Gordon Bell Prize for their study that used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.
ORNL is leading three research collaborations with fusion industry partners through the Innovation Network for FUSion Energy, or INFUSE, program that will focus on resolving technical challenges and developing innovative solutions to make practical fusion energy a reality.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.