The Department of Energy announced awards for 10 projects with private industry that will allow for collaboration with DOE national laboratories in accelerating fusion energy development.
A developing method to gauge the occurrence of a nuclear reactor anomaly has the potential to save millions of dollars.
The inside of future nuclear fusion energy reactors will be among the harshest environments ever produced on Earth. What’s strong enough to protect the inside of a fusion reactor from plasma-produced heat fluxes akin to space shuttles reentering Earth’s atmosphere?
As CASL ends and transitions to VERA Users Group, ORNL looks at the history of the program and its impact on the nuclear industry.
Oak Ridge National Laboratory scientists evaluating northern peatland responses to environmental change recorded extraordinary fine-root growth with increasing temperatures, indicating that this previously hidden belowground mechanism may play an important role in how carbon-rich peatlands respond to warming.
Combining expertise in physics, applied math and computing, Oak Ridge National Laboratory scientists are expanding the possibilities for simulating electromagnetic fields that underpin phenomena in materials design and telecommunications.
Joe Hagerman, ORNL research lead for buildings integration and controls, understands the impact building technology innovations can have during times of crisis. Over a decade ago, he found himself in the middle of one of the most devastating natural disasters of the century, Hurricane Katrina.
Scientists seeking ways to improve a battery’s ability to hold a charge longer, using advanced materials that are safe, stable and efficient, have determined that the materials themselves are only part of the solution.
Lithium, the silvery metal that powers smart phones and helps treat bipolar disorders, could also play a significant role in the worldwide effort to harvest on Earth the safe, clean and virtually limitless fusion energy that powers the sun and stars.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.