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A team from ORNL, joined by university students, recently traveled to the Ohio State University Research Reactor to conduct a novel experiment on nuclear thermal rocket fuel coatings — one that could help propel NASA’s astronauts to Mars faster and more efficiently.
In collaboration with the U.S. Department of Homeland Security’s Science and Technology Directorate, researchers at ORNL are evaluating technology to detect compounds emitted by pathogens and pests in agricultural products at the nation’s border.
Professionals from government and industry gathered at ORNL for the Nondestructive Assay Holdup Measurements Training Course for Nuclear Criticality Safety, a hands-on training in nondestructive assay, a technique for detecting and quantifying holdup without disturbing operations.
During his first visit to 91°µÍø, Energy Secretary Chris Wright compared the urgency of the Lab’s World War II beginnings to today’s global race to lead in artificial intelligence, calling for a “Manhattan Project 2.â€
Emrullah Aydin, an R&D staff associate in the Vehicle Power Electronics Research group at the Department of Energy’s 91°µÍø, has been elevated to senior member status in the Institute of Electrical and Electronics Engineers.
Vivek Sujan, a distinguished R&D scientist in the Applied Research for Mobility Systems group at ORNL, has been named a 2024 National Association of Inventors Fellow for his numerous transportation-related patents.
Researchers at the Department of Energy’s 91°µÍø are using non-weather data from the nationwide weather radar network to understand how to track non-meteorological events moving through the air for better emergency response.
During Hurricanes Helene and Milton, ORNL deployed drone teams and the Mapster platform to gather and share geospatial data, aiding recovery and damage assessments. ORNL's EAGLE-I platform tracked utility outages, helping prioritize recovery efforts. Drone data will train machine learning models for faster damage detection in future disasters.
A team of researchers at ORNL are using virtual reality to understand normal and abnormal human behavior in a given location – specifically, a nuclear reactor. As people move around their lives, they tend to do similar activities in the same
Researchers are using machine learning to provide a more complete picture of building geometries that include building height to within three meters of accuracy. This model not only provides building height for any building in the world, but it will also feed into LandScan and other large government datasets for planning and response.