Researchers at ORNL recently demonstrated a new technology to better control how power flows to and from commercial buildings equipped with solar, wind or other renewable energy generation.
When Hurricane Maria battered Puerto Rico in 2017, winds snapped trees and destroyed homes, while heavy rains transformed streets into rivers. But after the storm passed, the human toll continued to grow as residents struggled without electricity for months. Five years later, power outages remain long and frequent.
As climate change leads to larger and more frequent wildfires, researchers at ORNL are using sensors, drones and machine learning to both prevent fires and reduce their damage to the electric grid.
ORNL and the Tennessee Valley Authority, or TVA, are joining forces to advance decarbonization technologies from discovery through deployment through a new memorandum of understanding, or MOU.
Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.
A team of collaborators from ORNL, Google Inc., Snowflake Inc. and Ververica GmbH has tested a computing concept that could help speed up real-time processing of data that stream on mobile and other electronic devices.
ORNL has licensed its wireless charging technology for electric vehicles to Brooklyn-based HEVO. The system provides the world’s highest power levels in the smallest package and could one day enable electric vehicles to be charged as they are driven at highway speeds.
A new tool that simulates the energy profile of every building in America will give homeowners, utilities and companies a quick way to determine energy use and cost-effective retrofits that can reduce energy and carbon emissions.
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.
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.