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Small modular reactor computer simulation

Nuclear scientists at Oak Ridge National Laboratory are retooling existing software used to simulate radiation transport in small modular reactors, or SMRs, to run more efficiently on next-generation supercomputers. ORNL is working on various aspects of advanced SMR designs through s...

Joseph Lukens, Pavel Lougovski and Nicholas Peters (from left), researchers with ORNL’s Quantum Information Science Group, are examining methods for encoding photons with quantum information that are compatible with the existing telecommunications infrast
A team of researchers led by the Department of Energy’s Oak Ridge National Laboratory has demonstrated a new method for splitting light beams into their frequency modes. The scientists can then choose the frequencies they want to work with and encode photons with qu...
ORNL and EPRI built an enclosed welding system in a hot cell of ORNL’s Radiochemical Engineering Development Center. C. Scott White (ORNL) performs operations with remotely controlled manipulators and cameras.

Scientists of the Department of Energy’s Light Water Reactor Sustainability Program (LWRS) and partners from the Electric Power Research Institute (EPRI) have conducted the first weld tests to repair highly irradiated materials at DOE’s Oak Ridge National Laboratory.

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A team led by the Department of Energy’s Oak Ridge National Laboratory has uncovered how certain soil microbes cope in a phosphorus-poor environment to survive in a tropical ecosystem. Their novel approach could be applied in other ecosystems to study various nutrient limitations and inform agriculture and terrestrial biosphere modeling.
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

An example of a spiking neural network shows how data can be classified using the neuromorphic device. Credit: Catherine Schuman and Margaret Drouhard/Oak Ridge National Laboratory, U.S. Dept. of Energy
For smarter data management and analysis, researchers have developed a low-power neuromorphic device based on spiking neural networks that can quickly and more efficiently analyze and classify data.
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To improve models for drilling, hydraulic fracturing and underground storage of carbon dioxide, Oak Ridge National Laboratory scientists used neutrons to understand how water flows through fractured rock.