PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
With the launch of a large research barge, PNNL and collaborators took another significant step to improve offshore wind forecasting that will lower risk and cost associated with offshore wind energy development.
The nation is closer to its offshore wind energy goals than ever before, but better wind forecasting is still needed. To address this challenge, PNNL and collaborators are charting a new course with help from novel technology.
Pacific Northwest National Laboratory launches the Training Outreach and Recruitment for Cybersecurity Hydropower program at the University of Texas at El Paso.
A team of researchers at PNNL has created a publicly available Hydropower eLibrary to improve access to information that could help streamline the FERC environmental review and licensing process.
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.
