We have a data problem. Humanity is now generating more data than it can handle; more sensors, smartphones, and devices of all types are coming online every day and contributing to the ever-growing global dataset.
OAK RIDGE, Tenn., Feb. 19, 2020 — The U.S. Department of Energy’s Oak Ridge National Laboratory and the Tennessee Valley Authority have signed a memorandum of understanding to evaluate a new generation of flexible, cost-effective advanced nuclear reactors.
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
Rigoberto “Gobet” Advincula has been named Governor’s Chair of Advanced and Nanostructured Materials at Oak Ridge National Laboratory and the University of Tennessee.
Researchers across the scientific spectrum crave data, as it is essential to understanding the natural world and, by extension, accelerating scientific progress.
For nearly three decades, scientists and engineers across the globe have worked on the Square Kilometre Array (SKA), a project focused on designing and building the world’s largest radio telescope. Although the SKA will collect enormous amounts of precise astronomical data in record time, scientific breakthroughs will only be possible with systems able to efficiently process that data.
Researchers at Oak Ridge National Laboratory demonstrated that an additively manufactured polymer layer, when applied to carbon fiber reinforced plastic, or CFRP, can serve as an effective protector against aircraft lightning strikes.
Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.