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Researchers at ORNL have developed a tool that gives builders a quick way to measure, correct and certify level foundations. FLAT, or the Flat and Level Analysis Tool, examines a 360-degree laser scan of a construction site using ORNL-developed segmentation algorithms and machine learning to locate uneven areas on a concrete slab. 

Scientists at ORNL have developed a vacuum-assisted extrusion method that reduces �鶹Ӱ�� porosity by up to 75% in large-scale 3D-printed polymer parts. This new technique addresses the critical issue of porosity in large-scale prints but also paves the way for stronger composites. 

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. 

 

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.”

By editing the polymers of discarded plastics, ORNL chemists have found a way to generate new macromolecules with more valuable properties than those of the starting material.

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.

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 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.

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.

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.