A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.
Drawing from his experience during the pandemic, Domenick Leto recognizes the need for the United States to have inexpensive, reliable capabilities to combat any type of disruption to national security, including nationwide medical emergencies. Leto and colleagues received a patent for a simple, inexpensive way to sterilize masks, plastic, and medical equipment from the COVID-19 virus.
Researchers at ORNL became the first to 3D-print large rotating steam turbine blades for generating energy in power plants.
For years, Duane Starr led workshops at ORNL to help others from across the U.S. government understand uranium processing technologies. After his retirement, Starr donated a 5-foot-tall working model, built in his garage, that demonstrates vibration harmonics, consistent with operation of a super critical gas centrifuge rotor, a valuable resource to ongoing ORNL-led workshops.
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
A 19-member team of scientists from across the national laboratory complex won the Association for Computing Machinery’s 2023 Gordon Bell Special Prize for Climate Modeling for developing a model that uses the world’s first exascale supercomputer to simulate decades’ worth of cloud formations.
A team of eight scientists won the Association for Computing Machinery’s 2023 Gordon Bell Prize for their study that used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.
Four researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.
Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.