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ORNL researchers Todd Toops, Charles Finney, and Melanie DeBusk (left to right) hold an example of a particulate filter used to collect harmful emissions in vehicles.

Researchers are looking to neutrons for new ways to save fuel during the operation of filters that clean the soot, or carbon and ash-based particulate matter, emitted by vehicles. A team of researchers from the Energy and Transportation Science Division at the Department of En...

Germina Ilas (left) and Ian Gauld review spent fuel data entries in the SFCOMPO 2.0 database.
Oak Ridge National Laboratory provided significant contributions and coordination in the development of the Nuclear Energy Agency’s (NEA’s) recently released Spent Fuel Isotopic Composition (SFCOMPO) 2.0—the world’s largest open database for spent
ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

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

California charging EV station map

Officials responsible for anticipating the demand for electric vehicle charging stations could get help through a sophisticated new method developed at Oak Ridge National Laboratory. The method considers electric vehicle volume and the random timing of vehicles arriving at cha...