Quantum precision reached in modeling molten salt behavior
A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.
A new tool from Oak Ridge National Laboratory can help planners, emergency responders and scientists visualize how flood waters will spread for any scenario and terrain.
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
The University of Texas at San Antonio (UTSA) has formally launched the Cybersecurity Manufacturing Innovation Institute (CyManII), a $111 million public-private partnership.
Planning for a digitized, sustainable smart power grid is a challenge to which Suman Debnath is using not only his own applied mathematics expertise, but also the wider communal knowledge made possible by his revival of a local chapter of the IEEE professional society.
ORNL and Department of Energy officials dedicated the launch of two clean energy research initiatives that focus on the recycling and recovery of advanced manufacturing materials and on connected and
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.
ORNL researchers have developed an intelligent power electronic inverter platform that can connect locally sited energy resources such as solar panels, energy storage and electric vehicles and smoothly interact with the utility power grid.
Scientists at Oak Ridge National Laboratory have demonstrated a direct relationship between climate warming and carbon loss in a peatland ecosystem.
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.