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Media Contacts
Researchers at Oak Ridge National Laboratory have developed free data sets to estimate how much energy any building in the contiguous U.S. will use in 2100. These data sets provide planners a way to anticipate future energy needs as the climate changes.
Oak Ridge National Laboratory scientists ingeniously created a sustainable, soft material by combining rubber with woody reinforcements and incorporating “smart” linkages between the components that unlock on demand.
Building innovations from ORNL will be on display in Washington, D.C. on the National Mall June 7 to June 9, 2024, during the U.S. Department of Housing and Urban Development’s Innovation Housing Showcase. For the first time, ORNL’s real-time building evaluator was demonstrated outside of a laboratory setting and deployed for building construction.
Scientists at ORNL completed a study of how well vegetation survived extreme heat events in both urban and rural communities across the country in recent years. The analysis informs pathways for climate mitigation, including ways to reduce the effect of urban heat islands.
Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
The Society of Manufacturing Engineers has honored three Oak Ridge National Laboratory researchers with the 2024 SME Susan Smyth Outstanding Young Manufacturing Engineer Award.
Kate Evans, director for the Computational Sciences and Engineering Division at ORNL, has been awarded the 2024 Society for Industrial and Applied Mathematicians Activity Group on Mathematics of Planet Earth Prize.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.