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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

Cody Lloyd stands in front of images of historical nuclear field testing. The green and red dots are the machine learning algorithm recognizing features in the image. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Cody Lloyd became a nuclear engineer because of his interest in the Manhattan Project, the United States’ mission to advance nuclear science to end World War II. As a research associate in nuclear forensics at ORNL, Lloyd now teaches computers to interpret data from imagery of nuclear weapons tests from the 1950s and early 1960s, bringing his childhood fascination into his career

Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

After completing a bachelor’s degree in biology, Toya Beiswenger didn’t intend to go into forensics. But almost two decades later, the nuclear security scientist at ORNL has found a way to appreciate the art of nuclear forensics.

Two researchers standing back to back in a grassy area

When geoinformatics engineering researchers at the Department of Energy’s Oak Ridge National Laboratory wanted to better understand changes in land areas and points of interest around the world, they turned to the locals — their data, at least.

This newly manufactured fixed guide vane of a hydropower turbine system was printed at the DOE Manufacturing Demonstration Facility at ORNL. Credit: Genevieve Martin/ORNL, U.S Dept. of Energy

A new report published by ORNL assessed how advanced manufacturing and materials, such as 3D printing and novel component coatings, could offer solutions to modernize the existing fleet and design new approaches to hydropower.

Researchers at Oak Ridge National Laboratory developed an eco-friendly foam insulation for improved building efficiency. Credit: Chad Malone/ORNL, U.S. Dept. of Energy

Scientists at ORNL developed a competitive, eco-friendly alternative made without harmful blowing agents.

 Leadership from Oak Ridge National Laboratory and the National Energy Technology Laboratory signed a memorandum of understanding to jointly explore carbon management strategies in the Appalachian region. Credit: NETL, U.S. Dept. of Energy

ORNL is teaming with the National Energy Technology Laboratory to jointly explore a range of technology innovations for carbon management and strategies for economic development and sustainable energy transitions in the Appalachian region.

A LiDAR survey of a local stream collected from one of ORNL’s drones. Credit: Andrew Duncan/ORNL, U.S. Dept. of Energy

The Autonomous Systems group at ORNL is in high demand as it incorporates remote sensing into projects needing a bird’s-eye perspective.

A new license to U2opia pairs two technologies developed in ORNL’s Cyber Resilience and Intelligence Division: Situ and Heartbeat. Credit: ORNL, U.S. Dept. of Energy

U2opia Technology, a consortium of technology and administrative executives with extensive experience in both industry and defense, has exclusively licensed two technologies from ORNL that offer a new method for advanced cybersecurity monitoring in real time.

State and Local Economic Development Award

A partnership of ORNL, the Tennessee Department of Economic and Community Development, the Community Reuse Organization of East Tennessee and TVA that aims to attract nuclear energy-related firms to Oak Ridge has been recognized with a state and local economic development award from the Federal Laboratory Consortium.