In a study off the West Coast, researchers find that although seabirds generally soar underneath the height of possible future wind turbine blades, more work is being done to fully understand seabird flight behavior.
A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
Researchers seek to bring down costs, address potential environmental risks and maximize the benefits of harnessing wind energy above the deep waters of the Pacific.
In 2006, battery research was practically non-existent at PNNL. Today, the lab is lauded for its battery research. How did PNNL go from a new player to a leader in state-of-the-art storage for EVs and the grid?
The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.
Two renewable energy approaches—enhanced geothermal systems and floating offshore wind energy—get new focus as Energy Earthshot™ Research Centers at PNNL.
Summer is for science! PNNL’s interns are diving into science and technology and getting a front-row view of the research and development of a national laboratory.
PNNL researchers are helping to better define the need for grid energy storage in future clean energy scenarios, as well as working to improve technologies for storing renewable energy so it's available when and where it's needed.
To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.
