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ORNL's Communications team works with news media seeking information about the laboratory. Media may use the resources listed below or send questions to news@ornl.gov.

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Illustration of a glowing black box emitting digital particles that form into a 3D model of an electrical grid infrastructure, set against a background of binary code and data visualizations.

Researchers at Oak Ridge National Laboratory have developed a modeling method that uses machine learning to accurately simulate electric grid behavior while protecting proprietary equipment details. The approach overcomes a key barrier to accurate grid modeling, helping utilities plan for future demand and prevent blackouts. 

 

Secretary Wright leans over red computer door, signing with silver sharpie as ORNL Director Stephen Streiffer looks on

During his first visit to Oak Ridge National Laboratory, 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.”

Autonomous Configurable Component Evaluation Power Test platform, called ACCEPT, enabling automated characterization of semiconductor devices.

Researchers at Oak Ridge National Laboratory have developed a new automated testing capability for semiconductor devices, which is newly available to researchers and industry partners in the Grid Research Integration and Deployment Center.

Photo is a high aerial view of lake superior through the clouds

Researchers at Stanford University, the European Center for Medium-Range Weather Forecasts, or ECMWF, and ORNL used the lab’s Summit supercomputer to better understand atmospheric gravity waves, which influence significant weather patterns that are difficult to forecast. 

Man is flying drone in hurricane aftermath, holding the controller

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 looking at computers, working with a bright blue box in the middle of the table

Researchers at ORNL are using microwave radar reflection to nondestructively detect and measure the moisture content of materials within walls without removing drywall or cladding. This also expedites the moisture identification process and enables mold growth to be treated in the early stages.

Two scientists are standing in the lab pointing at an object on the table, both wearing glasses.

ORNL, as a partner in the DOE’s Stor4Build Consortium, is co-leading research with several national laboratories to develop thermal energy storage to complement electrical battery storage and recently hosted a two-day workshop focused on advancing these technologies.

Researcher Maximiliano Ferrari is kneeling down next to an emulator in the networked microgrids laboratory at the Grid Research Integration and Deployment Center

Maximiliano Ferrari, a researcher in the Grid Systems Architecture group at the Department of Energy’s Oak Ridge National Laboratory, has been elevated to prestigious senior membership in the Institute of Electrical and Electronics Engineers. 

ORNL computing staff members Hector Suarez (middle) and William Castillo (right) talk HPC at the Tapia Conference career fair in San Diego, California. Credit: ORNL, U.S. Dept of Energy

The National Center for Computational Sciences, located at the Department of Energy’s Oak Ridge National Laboratory, made a strong showing at computing conferences this fall. Staff from across the center participated in numerous workshops and invited speaking engagements.

3D map of Washington, D.C. that is a weather model of neighborhood during heat waves. The map is red and green indicating which buildings are giving off more heat
Scientists at ORNL have developed a first-ever urban heat wave simulation that takes into account the compounding effects from building infrastructure. The method provides a more accurate picture of the impacts from excessive heat on at-risk