PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
The diversity and function of organic matter in rivers at a large scale are influenced by factors, such as the types of vegetation covering the land, the energy characteristics, and the breakdown potential of the molecules.
Through collaboration with the Department of Homeland Security Soft Target Engineering to Neutralize the Threat Reality Center of Excellence, PNNL is advancing research and development of tools and methodologies to protect crowded places.
PNNL’s ARENA test bed analyzes how electrical cables degrade in extreme environments and how nondestructive examination inspection technologies can detect and locate damage.
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.
Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.
ICON science is a Department of Energy-developed framework to enhance scientific outcomes via more intentional design of research efforts across all domains of science.
Integrating hydrogeology and biogeochemistry are required to model the dynamics of geochemical processes occurring in river corridor zones where groundwater and surface water mix.
Principles derived from coastal wetlands to describe wetland channel cross-sections were applicable to the Columbia River estuary, but not the tidal river.
