Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.
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.
Hailong Wang is a non-federal co-lead for the Arctic Systems Interactions Collaboration Team that will explore the Arctic’s dynamic interconnected systems.
PNNL is working with the Port of Seattle and Seattle City Light to assess the risks of long-term hydrogen storage that can bring clean power for decarbonization.
A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.
Extreme winter storms are growing wetter and changing shape in the Western United States—such changes could compromise infrastructure designed to withstand only so much water.
Scientists used a new analysis approach—coupled laser ablation sampling and capillary absorption spectroscopy—to gather more information on how the rhizosphere processes carbon.