In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.
Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.
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.
Oak Ridge National Laboratory researchers have identified the most energy-efficient 2024 model year vehicles available in the United States, including electric and hybrids, in the latest edition of the Department of Energy’s Fuel Economy Guide.
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.
ORNL and Caterpillar Inc. have entered into a cooperative research and development agreement, or CRADA, to investigate using methanol as an alternative fuel source for four-stroke internal combustion marine engines.
Within the Department of Energy’s National Transportation Research Center at ORNL’s Hardin Valley Campus, scientists investigate engines designed to help the U.S. pivot to a clean mobility future.
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.