Developed at PNNL, Shear Assisted Processing and Extrusion, or ShAPE™, uses significantly less energy and can deliver components like wire, tubes and bars 10 times faster than conventional extrusion, with no sacrifice in quality.
PNNL has received 119 R&D 100 Awards since 1969, when the laboratory began submitting entries in the contest that recognizes top 100 inventions each year.
An energy-efficient method to extrude metal components wins Association of Washington Business Green Manufacturing Award. PNNL’s Shear Assisted Processing and Extrusion™ technology consumes less energy and enhances material properties.
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.
Researchers developed two solutions for air-conditioning—a novel, energy-efficient dehumidification system and a technology to detect refrigerant leaks. Both help increase energy-efficiency and reduce costs.
PNNL scientists developed a new, tiny battery and tag to track younger, smaller species, to evaluate behavior and estimate survival during downstream migration.
PNNL and four other national laboratories executed the Hydropower Value Study to examine hydropower operations in different regions of the United States.
Study says planners need to account for climate impacts on renewable energy during capacity development planning to fully understand investment implications to the power sector.
Through two U.S. Department of Energy funding calls awarded in 2020, PNNL is partnering with industry and academia to advance battery materials and processes.
PNNL has published a report that sets the foundation for modeling gaps and technical challenges in optimizing hydropower operations for both energy production and water management.
California and other areas of the U.S. Southwest may see less future winter precipitation than previously projected by climate models, according to new research that corrects for a long-standing model error: the double-ITCZ bias.