Researchers at PNNL advised elementary and middle school student teams with their problem-solving research for the FIRST® LEGO® League robotics competitions.
By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.
New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.
The demand for energy is growing—and so is the technology supporting it. However, future development of power generation technologies could be affected by a key factor: material supply.
A new digital twin platform can help hydropower dam operators by providing accurate and predictive models of physical turbines that improve facilities and enhance reliability.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Although climate change may bring increased precipitation to many parts of the United States, some areas may face drier conditions and lower streamflow, resulting in decreased hydropower generation.
Aerosol particles imbue climate models with uncertainty. New work by PNNL researchers reveals where in the world and under what conditions new particles are born.
Accessing groundwater may become more difficult—and more expensive—as groundwater supplies become increasingly scarce and underground aquifer levels fall.
