- Number 398 |
- September 30, 2013
Some 10 shrill alarms were going off at once. In what looked and sounded like a nuclear plant control room, it appeared that there had been a steam generator tube rupture. In charge of solving the problem: A pair of neuroscience graduate students doing summer research at DOE's Idaho National Laboratory with the Human Factors group — a team of researchers who study the intersections between minds and machines.The Human Systems Simulation Lab (HSSL) at INL is a good facsimile of a real nuclear control room. Human factors researchers use the HSSL as a test-bed for new control features. The capability, which is supported by the U.S. Department of Energy Light Water Reactor Sustainability (LWRS) Program, is now helping Duke Energy embark on an upgrade project for several of its nuclear plant control rooms.
Scientists on the Cryogenic Dark Matter Search have set the strongest limits in the world for the detection of a light dark-matter particle with a mass below 6 billion electronvolts, or about six times the mass of a proton.
The composition of dark matter, which accounts for more than 80 percent of all matter in the universe, could be as complicated as the makeup of ordinary matter. In the past, many experiments have focused on searching for dark-matter particles that are heavy. But recent experimental results and new theoretical models have provoked a strong interest in the search for light dark-matter particles.
A team of scientists has reported direct visualization of magnetic charge crystallization in an artificial spin ice material, a first in the study of a relatively new class of frustrated artificial magnetic materials-by-design known as “Artificial Spin Ice.” These charges are analogs to electrical charges with possible applications in magnetic memories and devices; in describing this class of materials, the new work demonstrates their utility.
Staff scientist Cristiano Nisoli at DOE's Los Alamos National Laboratory explained, “Magnetic technology generally concerns itself with manipulation of localized dipolar degrees of freedom,” he said. “The ability of building materials containing delocalized monopolar charges is very exciting with possible technological implications in data storage and computation.”
To improve the accuracy of solar power forecasting, research meteorologist Edwin Campos and his colleagues at DOE's Argonne National Laboratory have partnered with IBM to build a forecasting technology based on IBM's Watson supercomputer, made famous by its 2011 victory over human champions on the television quiz show Jeopardy!.
Campos hopes that the information he gains by integrating big data processing, machine learning and cloud modeling into a Watson-like platform will help grid managers and power plant operators develop more efficient strategies for allocating their resources to manage the unevenness of solar generation.