Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Shih-Chieh Kao, manager of the Water Power program at ORNL, has been named a fellow of the American Society of Civil Engineer’s Environmental & Water Resources Institute, or EWRI.
Colleen Iversen, ecosystem ecologist, group leader and distinguished staff scientist, has been named director of the Next-Generation Ecosystem Experiments Arctic, or NGEE Arctic, a multi-institutional project studying permafrost thaw and other climate-related processes in Alaska.
Computing pioneer Jack Dongarra has been elected to the National Academy of Sciences in recognition of his distinguished and continuing achievements in original research.
Xiao Wang, a research scientist at ORNL, has been named a senior member of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals. Wang works in the lab’s Computing and Computational Sciences Directorate’s Advanced Computing for Health Sciences Section.
Craig Blue, Defense Manufacturing Program Director at the Department of Energy’s Oak Ridge National Laboratory, was recently elected to a two-year term on the Institute for Advanced Composites Manufacturing Innovation Consortium Council, a body of professionals from academia, state governments, and national laboratories that provides strategic direction and oversight to IACMI.
ORNL has named Michael Parks director of the Computer Science and Mathematics Division within ORNL’s Computing and Computational Sciences Directorate. His hiring became effective March 13.
Merlin Theodore is one of eight new board members announced by President Biden; she will join the 25-member board for a six-year term.
Scientists at the Department of Energy’s Oak Ridge National Laboratory are leading a new project to ensure that the fastest supercomputers can keep up with big data from high energy physics research.
Laboratory Director Thomas Zacharia presented five Director’s Awards during Saturday night's annual Awards Night event hosted by UT-Battelle, which manages ORNL for the Department of Energy.
A new deep-learning framework developed at ORNL is speeding up the process of inspecting additively manufactured metal parts using X-ray computed tomography, or CT, while increasing the accuracy of the results. The reduced costs for time, labor, maintenance and energy are expected to accelerate expansion of additive manufacturing, or 3D printing.