A software package, 10 years in the making, that can predict the behavior of nuclear reactors’ cores with stunning accuracy has been licensed commercially for the first time.
The techniques Theodore Biewer and his colleagues are using to measure whether plasma has the right conditions to create fusion have been around awhile.
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.
As the second-leading cause of death in the United States, cancer is a public health crisis that afflicts nearly one in two people during their lifetime.
Researchers across the scientific spectrum crave data, as it is essential to understanding the natural world and, by extension, accelerating scientific progress.
The Department of Energy’s Oak Ridge National Laboratory is collaborating with industry on six new projects focused on advancing commercial nuclear energy technologies that offer potential improvements to current nuclear reactors and move new reactor designs closer to deployment.
The United Kingdom’s National Nuclear Laboratory and the U.S. Department of Energy’s Oak Ridge National Laboratory have agreed to cooperate on a wide range of nuclear energy research and development efforts that leverage both organizations’ unique expertise and capabilities.
A tiny vial of gray powder produced at the Department of Energy’s Oak Ridge National Laboratory is the backbone of a new experiment to study the intense magnetic fields created in nuclear collisions.
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the