This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.
Zhiqun (Daniel) Deng, Lab Fellow at PNNL, has been named a fellow of the American Society of Mechanical Engineers, an honor that recognizes outstanding engineering achievements.
PNNL’s science and technology helps hydropower operators detect, prevent and recover from cyberattacks while protecting a source of electricity that enhances grid reliability and resilience.
By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.
A new analysis shows how renewable energy sources like solar, wind and hydropower respond to climate patterns, and how utilities can use this data to save money and invest in energy storage.
Alicia Amerson's passion for science communication, expertise in marine mammal research, and experience in wildlife photography provide a robust foundation for her new role with the Clallam County Marine Resources Committee.
A new digital twin platform can help hydropower dam operators by providing accurate and predictive models of physical turbines that improve facilities and enhance reliability.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Although climate change may bring increased precipitation to many parts of the United States, some areas may face drier conditions and lower streamflow, resulting in decreased hydropower generation.
PNNL’s patented Shear Assisted Processing and Extrusion (ShAPE™) technique is an advanced manufacturing technology that enables better-performing materials and components while offering opportunities to reduce costs and energy consumption.
With her broad experience and background, Starr Abdelhadi was selected from many applicants to join the Women in IT Networking at SC (WINS) program for Super Computing 2024 (SC24).
New methodological approach demonstrates how to assess the economic value, including non-traditional value streams, of converting non-powered dams to hydroelectric facilities.
