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Media Contacts
ORNL researchers modeled how hurricane cloud cover would affect solar energy generation as a storm followed 10 possible trajectories over the Caribbean and Southern U.S.
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.
A key industrial isotope, iridium-192, has not been produced in the U.S. in almost 20 years. DOE's Isotope Program and QSA Global Inc. announced a joint product development agreement to initiate U.S. production of iridium-192.
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.
The 21st Symposium on Separation Science and Technology for Energy Applications, Oct. 23-26 at the Embassy Suites by Hilton West in Knoxville, attracted 109 researchers, including some from Austria and the Czech Republic. Besides attending many technical sessions, they had the opportunity to tour the Graphite Reactor, High Flux Isotope Reactor and both supercomputers at ORNL.
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.
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.