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Media Contacts
College intern Noah Miller is on his 3rd consecutive internship at ORNL, currently working on developing an automated pellet inspection system for Oak Ridge National Laboratory’s Plutonium-238 Supply Program. Along with his success at ORNL, Miller is also focusing on becoming a mentor for kids, giving back to the place where he discovered his passion and developed his skills.
Since 2019, a team of NASA scientists and their partners have been using NASA’s FUN3D software on supercomputers located at the Department of Energy’s Oak Ridge Leadership Computing Facility to conduct computational fluid dynamics simulations of a human-scale Mars lander. The team’s ongoing research project is a first step in determining how to safely land a vehicle with humans onboard onto the surface of Mars.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.
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.
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.
Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.
Researchers at ORNL have been leading a project to understand how a high-altitude electromagnetic pulse, or EMP, could threaten power plants.