PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
The first tidal turbine deployed in the Pacific Northwest at PNNL-Sequim showcases the Lab’s growing role as a regional center for marine energy research.
Researchers investigated how stable nanoparticle suspensions form using facet engineering on hematite nanoparticles, demonstrating that controlling the faceting of nanoparticles can effectively maintain particle dispersity.
Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.
Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
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.
Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.
PNNL researchers developed the dummy payload to evaluate the performance of marine energy device prototypes in the Powering the Blue Economy: Ocean Observing Prize Competition.
