Two new publications provide emergency response agencies with critical insights into commercially available unmanned ground vehicles used for hazardous materials response.
Engineers at PNNL devised a system that allows radar antennae to maintain stable orientation while mounted on platforms in open water that pitch and roll unpredictably. They were recently invited to participate in DOE's I-Corps program.
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.
A team from PNNL contributed several articles to the Domestic Preparedness Journal showcasing recent efforts to explore the emergency management and artificial intelligence research and development landscape.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
At the National Homeland Security Conference, researchers shared how partnerships and emerging technologies like artificial intelligence can play a key role in emergency management preparedness and response.
PNNL advisors joined a panel of Washington State emergency management personnel to discuss how partnerships with national laboratories are enabling science and technology solutions.
At the GovAI Summit 2023, Ryan Eddy joined the Department of Homeland Security Science and Technology Directorate on a panel highlighting artificial intelligence impacts and opportunities in the field of emergency management.
A team of researchers received an award for their contributions to improving the operational readiness and safety posture of the firefighter community by conducting a rigorous evaluation of commercially available equipment.
Ann Lesperance has been invited to continue her role on the Domestic Preparedness advisory board, which convenes multidisciplinary subject matter experts to support the Domestic Preparedness Journal's editorial plan.
Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.
PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
