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ORNL researchers are working to make EV charging more resilient by developing algorithms to deal with both internal and external triggers of charger failure. This will help charging stations remain available to traveling EV drivers, reducing range anxiety.
Although he built his career around buildings, Fengqi “Frank” Li likes to break down walls. Li was trained as an architect, but he doesn’t box himself in. Currently he is working as a computational developer at ORNL. But Li considers himself a designer. To him, that’s less a box than a plane – a landscape scattered with ideas, like destinations on a map that can be connected in different ways.
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.
The American Society of Heating, Refrigeration and Air-Conditioning Engineers, or ASHRAE, selected Jason DeGraw, a researcher with ORNL, as one of 23 members elevated to Fellow during its 2024 winter conference.
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.
ORNL researchers have developed a novel way to encapsulate salt hydrate phase-change materials within polymer fibers through a coaxial pulling process. The discovery could lead to the widespread use of the low-carbon materials as a source of insulation for a building’s envelope.
Four ORNL teams and one researcher were recognized for excellence in technology transfer and technology transfer innovation.
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.
Karen White, who works in ORNL’s Neutron Science Directorate, has been honored with a Lifetime Achievement Award.