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Three staff members in Oak Ridge National Laboratory’s Fusion and Fission Energy and Science Directorate (FFESD) have moved into newly established roles facilitating communication and program management with sponsors of the directorate’s Nuclear Energy and Fuel Cycle Division.

Three staff members in ORNL’s Fusion and Fission Energy and Science Directorate have moved into newly established roles facilitating communication and program management with sponsors of the directorate’s Nuclear Energy and Fuel Cycle Division. 

ORNL researchers are establishing a digital thread of data, algorithms and workflows to produce a continuously updated model of earth systems.

Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.

 

Frontier, the fastest supercomputer in the world, provides expansive and energy-efficient power, which gives scientists the capability to train large AI models in a responsible way.

ORNL is home to the world's fastest exascale supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations. 

Photo 1: Event organizers from the Nuclear Energy Fuel Cycle Division. Credit: Carol Morgan/ORNL, U.S. Dept. of Energy

The heat is on at this year’s Molten Salt Reactor Workshop – where top research and industry minds are melding to advance development on molten salt technology – at ORNL.   

Professional women in the IAEA’s Lise Meitner Programme 2023 cohort and supporters assembled at ORNL. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory hosted the second  2023 cohort of the International Atomic Energy Agency’s Lise Meitner Programme in October.

The OpeN-AM experimental platform, installed at the VULCAN instrument at ORNL’s Spallation Neutron Source, features a robotic arm that prints layers of molten metal to create complex shapes. This allows scientists to study 3D printed welds microscopically. Credit: Jill Hemman, ORNL/U.S. Dept. of Energy

Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of its Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making. Credit: Rachel Green/ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.

Director of ORNL’s AI Initiative Prasanna Balaprakash addresses attendees at the Generative AI for ORNL Science Workshop. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory hosted its Smoky Mountains Computational Science and Engineering Conference for the first time in person since the COVID pandemic broke in 2020. The conference, which celebrated its 20th consecutive year, took place at the Crowne Plaza Hotel in downtown Knoxville, Tenn., in late August.

Cadet Elyse Wages, Mike Shaffer and Amanda Sandifer pose with a collected sample of air. Credit: Liz Neunsinger/ORNL, U.S. Dept. of Energy

Cadet Elyse Wages, a rising junior at the United States Air Force Academy, visited ORNL with one goal in mind: collect air.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine