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Media Contacts
Wildfires have shaped the environment for millennia, but they are increasing in frequency, range and intensity in response to a hotter climate. The phenomenon is being incorporated into high-resolution simulations of the Earth’s climate by scientists at the Department of Energy’s Oak Ridge National Laboratory, with a mission to better understand and predict environmental change.
As extreme weather devastates communities worldwide, scientists are using modeling and simulation to understand how climate change impacts the frequency and intensity of these events. Although long-term climate projections and models are important, they are less helpful for short-term prediction of extreme weather that may rapidly displace thousands of people or require emergency aid.
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
Simulations performed on the Summit supercomputer at ORNL revealed new insights into the role of turbulence in mixing fluids and could open new possibilities for projecting climate change and studying fluid dynamics.
Innovations in artificial intelligence are rapidly shaping our world, from virtual assistants and chatbots to self-driving cars and automated manufacturing.
An innovative and sustainable chemistry developed at ORNL for capturing carbon dioxide has been licensed to Holocene, a Knoxville-based startup focused on designing and building plants that remove carbon dioxide
Rigoberto Advincula, a renowned scientist at ORNL and professor of Chemical and Biomolecular Engineering at the University of Tennessee, has won the Netzsch North American Thermal Analysis Society Fellows Award for 2023.
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
Computing pioneer Jack Dongarra has been elected to the National Academy of Sciences in recognition of his distinguished and continuing achievements in original research.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.