Innovative Energy Technologies Move Closer to Commercial Use
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.
Riveting Technology Enables Lightweight Magnesium Fasteners for Fuel Efficiency
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.
Friction Stir Processing Gets a Boost from New Machine Learning Model
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
New Research Effort Could Boost Nuclear Fuel Performance
Researchers have begun a series of experiments that could bring about more energy for grid, thanks to a special delivery of 11 "high burnup" rods.
Weak Electric Fields Can Profoundly Influence Chemical Physics Phenomena
Delivering an integrated quantum-mechanical and experimental perspective on the effects of both intrinsic and externally applied electric fields at atomic-scale interfaces.
Cooking Up a Conductive Alternative to Copper with Aluminum
The first-ever simulation of aluminum conductivity offers a recipe for an inexpensive, lightweight alternative to copper.
Aluminum Alloy Manufacturing Now 50 Percent More Energy Efficient
A new extrusion process eliminates pre-heating to provide significant energy savings during production of extruded aluminum alloys.
Designing Next-Generation Metals, One Atom at a Time
Direct visualization of metal atoms during shear deformation has broad applications from battery design to vehicle lightweighting.
INFUSE-ing Fusion Energy with Solid Phase Processing
PNNL will partner with Commonwealth Fusion Systems to develop innovative steel materials for fusion applications under the INFUSE program
Leveraging Machine Learning to Accelerate Advanced Manufacturing R&D
The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.