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The ForWarn visualization tool was co-developed by ORNL with the U.S. Forest Service. The tool captures and analyzes satellite imagery to track impacts such as storms, wildfire and pests on forests across the nation.
Researchers at Oak Ridge National Laboratory used the Frontier supercomputer to train the world’s largest AI model for weather prediction, paving the way for hyperlocal, ultra-accurate forecasts. This achievement earned them a finalist nomination for the prestigious Gordon Bell Prize for Climate Modeling.
Three flights, five thousand miles and half a dozen clearances and permissions stood between Tetiana Maltseva and the Department of Energy’s Oak Ridge National Laboratory. When she finally arrived at the lab to represent Ukraine at the 2024 Nuclear Energy Management School, her vision was clear.
At ORNL, a group of scientists used neutron scattering techniques to investigate a relatively new functional material called a Weyl semimetal. These Weyl fermions move very quickly in a material and can carry electrical charge at room temperature. Scientists think that Weyl semimetals, if used in future electronics, could allow electricity to flow more efficiently and enable more energy-efficient computers and other electronic devices.
ORNL researchers completed successful testing of a gallium nitride transistor for use in more accurate sensors operating near the core of a nuclear reactor. This is an important technical advance particularly for monitoring new, compact.
A group of high school graduates and community college students visited ORNL to meet staff and find out just what goes on at a DOE national laboratory. The Job Shadow Day was arranged by tnAchieves, a student support organization that works to increase higher educational opportunities for students across Tennessee through scholarships and mentorship.
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.
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.
The Department of Energy’s Oak Ridge National Laboratory and Tennessee State University have signed a memorandum of understanding to strengthen research cooperation and provide diverse undergraduate students enriching educational research opportunities at the lab.