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Troy Carter, director of the Fusion Energy Division at 91����, leads efforts to make fusion energy a reality, overseeing key projects like MPEX and fostering public-private collaborations in fusion research. 

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. 

US ITER has completed delivery of all components for the support structure of the central solenoid, the 60-foot-tall superconducting magnet that is the “heart” of the ITER fusion machine. 

During his first visit to 91����, 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.”

Scientists designing the world’s first controlled nuclear fusion power plant, ITER, needed to solve the problem of runaway electrons, negatively charged particles in the soup of matter in the plasma within the tokamak, the magnetic bottle intended to contain the massive energy produced. Simulations performed on Summit, the 200-petaflop supercomputer at ORNL, could offer the first step toward a solution.

Researchers at the Department of Energy’s 91���� 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. 

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.