Filtered by Environmental Monitoring for Marine Energy, Graph and Data Analytics, Radiation Measurement, Solar Energy, Vehicle Energy Storage, Vehicle Technologies, Waste Processing, and Wind Energy
PNNL is working with national laboratories and academia to provide electric vehicle manufacturers with batteries that are more reliable, high-performing, safe, and less expensive.
A multi-institution research team led by PNNL is addressing curb usage management challenges in large urban areas by developing a city-scale dynamic curb use simulation tool and an open-source curb management platform.
The Interfacial Dynamics in Radioactive Environments and Materials (IDREAM) Energy Frontier Research Center (EFRC) conducts fundamental science to support innovations in retrieving and processing high-level radioactive waste.
By improving the Weather Research and Forecasting (WRF)-Solar model, this project aims to reduce forecast errors, improve sub-grid scale variability estimates, and more accurately estimate forecast uncertainty.
PNNL administers two research buoys for the U.S. Department of Energy that allows collection of wind meteorological and oceanographic data off the nation's coasts.
PNNL is leading a consortium that provides funding opportunities to the automotive industry for accelerating new lightweight technologies in on-highway vehicles.
PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.
A software suite for working with neutron activation rates measured in a nuclear fission reactor, an accelerator-based neutron source, or any neutron field to determine the neutron flux spectrum using a generalized least-squares approach.
PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.
Visual Sample Plan (VSP) is a software tool that supports the development of a defensible sampling plan based on statistical sampling theory and the statistical analysis of sample results to support confident decision making.