Helping hundreds of manufacturing industries and water-power facilities across the U.S. increase energy efficiency requires a balance of teaching and training, blended with scientific guidance and technical expertise. It’s a formula for success that ORNL researchers have been providing to DOE’s Better Plants Program for more than a decade.
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.
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.
Three ORNL intellectual property projects with industry partners have advanced in DOE's Office of Technology Transitions Making Advanced Technology Commercialization Harmonized, or Lab MATCH, prize, which encourages entrepreneurs to find actionable pathways that bring lab-developed intellectual property to market.
Students with a focus on building science will spend 10 weeks this summer interning at ORNL, the National Renewable Energy Laboratory and Pacific Northwest Laboratory as winners of the DOE’s Office of Energy Efficiency and Renewable Energy’s Building Technologies Office sixth annual JUMP into STEM finals competition.
A modeling analysis led by ORNL gives the first detailed look at how geothermal energy can relieve the electric power system and reduce carbon emissions if widely implemented across the United States within the next few decades.
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.
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.
Karen White, who works in ORNL’s Neutron Science Directorate, has been honored with a Lifetime Achievement Award.