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The agreement builds upon years of collaboration, including a 2016 effort using modeling tools developed at ORNL to predict the first six months of operations of TVA’s Watts Bar Unit 2 nuclear power plant. Credit: Andrew Godfrey/Oak Ridge National Laboratory, U.S. Dept. of Energy

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.

The configurational ensemble (a collection of 3D structures) of an intrinsically disordered protein, the N-terminal of c-Src kinase, which is a major signaling protein in humans. Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy.

Using the Titan supercomputer and the Spallation Neutron Source at the Department of Energy’s Oak Ridge National Laboratory, scientists have created the most accurate 3D model yet of an intrinsically disordered protein, revealing the ensemble of its atomic-level structures.

Small modular reactor computer simulation

In a step toward advancing small modular nuclear reactor designs, scientists at Oak Ridge National Laboratory have run reactor simulations on ORNL supercomputer Summit with greater-than-expected computational efficiency.

ORNL staff members (from left) Ashley Shields, Michael Galloway, Ketan Maheshwari and Andrew Miskowiec are collaborating on a project focused on predicting and analyzing crystal structures of new uranium oxide phases. Credit: Jason Richards/ORNL

Scientists at the Department of Energy’s Oak Ridge National Laboratory are working to understand both the complex nature of uranium and the various oxide forms it can take during processing steps that might occur throughout the nuclear fuel cycle.

Supercomputing-Memory_boost1.jpg

Scientists at Oak Ridge National Laboratory and Hypres, a digital superconductor company, have tested a novel cryogenic, or low-temperature, memory cell circuit design that may boost memory storage while using less energy in future exascale and quantum computing applications.