To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.
PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.
This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.
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 researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.
Twelve energy-related technologies developed at PNNL have been selected for additional technology maturation funding to help move them from the laboratory and field tests to the marketplace.
Superman may be known as the "Man of Steel," but scientific superheroes at the Department of Energy's Pacific Northwest National Laboratory are developing a novel approach for manufacturing metals with superior strength.
A PNNL study that evaluated the use of friction stir technology on stainless steel has shown that the steel resists erosion more than three times that of its unprocessed counterpart.
PNNL is advancing scientific frontiers and addressing challenges in energy, the environment and national security. So, in no particular order, here are PNNL's top 10 research accomplishments of 2018
