PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
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.
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.
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.
An evaluation of models and prediction tools for distributed wind turbines has unearthed data that can help potential users make the most informed decisions on upfront investments.
PNNL wind energy experts have published the Distributed Wind Market Report: 2022 Edition, supplying key findings that can help businesses, communities, and homeowners make informed decisions.
New study elucidates the complex relaxation kinetics of supercooled water using a pulsed laser heating technique at previously inaccessible temperatures.
