�鶹Ӱ��

ORNL set a new milestone in nuclear component �鶹Ӱ��, successfully testing two 3D-printed stainless steel experimental capsules at the lab’s High Flux Isotope Reactor. This achievement marks an important step in demonstrating that additively manufactured components can meet the rigorous safety standards required in nuclear applications. 

DOE’s Argonne, Idaho, and Oak Ridge National Laboratories co-hosted the AI x Nuclear Energy Executive Summit: Unleashing the Power for AI. It brought together leaders from national labs, tech companies and the nuclear energy industry to discuss how to meet AI’s energy needs and accelerate nuclear energy via AI.

A former intern for ORNL was selected to represent Tennessee presenting his research at the National Junior Science and Humanities Symposium. Langalibalele “Langa” Lunga, a senior at Farragut High School in Knoxville, Tennessee, interned with ORNL working on deep learning for fast scanning microscopy, a technique for capturing microscopic images more rapidly than traditional methods.

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications. 

ORNL’s Biological Monitoring and Abatement Program, or BMAP, is marking 40 years of helping steward the DOE’s 33,476 acres of land on which some of the nation’s most powerful science and technology missions are carried out. 

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. 

UT-Battelle has contributed up to $475,000 for the purchase and installation of advanced manufacturing equipment to support a program at Tennessee’s Oak Ridge High School that gives students direct experience with the AI- and robotics-assisted workplace of the future. 

Analyzing massive datasets from nuclear physics experiments can take hours or days to process, but researchers are working to radically reduce that time to mere seconds using special software being developed at the Department of Energy’s Lawrence Berkeley and Oak Ridge national laboratories.  

Research teams at the Department of Energy’s Oak Ridge National Laboratory received computing resource awards to train and test AI foundation models for science. A total of six ORNL projects were awarded allocations from the National Artificial Intelligence Research Resource, or NAIRR, pilot and the Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program to train their AI models.