ORNL researchers modeled how hurricane cloud cover would affect solar energy generation as a storm followed 10 possible trajectories over the Caribbean and Southern U.S.
ORNL’s Omer Onar and Mostak Mohammad will present on ORNL's wireless charging technology in DOE’s Office of Technology Transitions National Lab Discovery Series Tuesday, April 30.
ORNL’s Erin Webb is co-leading a new Circular Bioeconomy Systems Convergent Research Initiative focused on advancing production and use of renewable carbon from Tennessee to meet societal needs.
Shift Thermal, a member of Innovation Crossroads’ first cohort of fellows, is commercializing advanced ice thermal energy storage for HVAC, shifting the cooling process to be more sustainable, cost-effective and resilient. Shift Thermal wants to enable a lower-cost, more-efficient thermal energy storage method to provide long-duration resilient cooling when the electric grid is down.
Scientists at ORNL have developed 3D-printed collimator techniques that can be used to custom design collimators that better filter out noise during different types of neutron scattering experiments
Astrophysicists at the State University of New York, Stony Brook and University of California, Berkeley, used the Oak Ridge Leadership Computing Facility’s Summit supercomputer to compare models of X-ray bursts in 2D and 3D.
Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.
Technology Transfer staff from Department of Energy’s Oak Ridge National Laboratory attended the 2024 Consumer Electronics Show, or CES, in Las Vegas, Jan. 8–12.
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
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.