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 investigating water-lean solvents for carbon dioxide capture identifies the unique chemistry possible with their use, may lead to new design principles that move beyond single carbon capture.
There are many ways that researchers at PNNL bring unique perspectives to the field of distributed wind. One is the fact that PNNL's distributed wind projects are all led by women.
Department of Energy’s Advanced Research Projects Agency-Energy selects PNNL project to help accelerate the development of marine carbon dioxide removal technologies.
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.
The Distributed Wind Market Report provides market statistics and analysis, along with insights into market trends and characteristics of wind technologies used as distributed energy resources.
Department of Energy, Office of Science Director Asmeret Asefaw Berhe visited PNNL to learn about the Lab’s drive to conduct discovery science, commitment to science for an equitable future, and development of a diversified STEM workforce.
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.
