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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.

AIRES 4 attendees hailing from seven national laboratories and from academia met to discuss robust engineering for digital twins. Credit: Pradeep Ramuhalli/ORNL, U.S. Dept. of Energy

ORNL hosted its fourth Artificial Intelligence for Robust Engineering and Science, or AIRES, workshop from April 18-20. Over 100 attendees from government, academia and industry convened to identify research challenges and investment areas, carving the future of the discipline.

Steven Hamilton. Credit: Genevieve Martin/ORNL.

As renewable sources of energy such as wind and sun power are being increasingly added to the country’s electrical grid, old-fashioned nuclear energy is also being primed for a resurgence.

Mickey Wade, associate laboratory director for the Fusion and Fission Energy and Science Directorate, addresses attendees of an event to celebrate the licensing of an augmented reality technology to Teletrix. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A method using augmented reality to create accurate visual representations of ionizing radiation, developed at ORNL, has been licensed by Teletrix, a firm that creates advanced simulation tools to train the nation’s radiation control workforce.

Researcher Chase Joslin uses Peregrine software to monitor and analyze a component being 3D printed at the Manufacturing Demonstration Facility at ORNL. Credit: Luke Scime/ORNL, U.S. Dept. of Energy.

Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.

Computing—Routing out the bugs

A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool