ORNL researchers recognized as among world’s most cited
As high-tech companies ramp up construction of massive data centers to meet the business boom in artificial intelligence, one component is becoming an increasingly rare commodity: electricity. Since its formation in 2004, the OLCF has fielded five generations of world-class supercomputing systems that have produced a nearly 2,000 times reduction in energy usage per floating point operation per second, or flops. With decades of experience in making HPC more energy efficient, the OLCF may serve as a resource for best “bang for the buck” practices in a suddenly burgeoning industry.
Researchers at ORNL are using satellite images of homes under construction to address gaps in census data, especially in areas like Sub-Saharan Africa. By analyzing these images, they estimate dwelling sizes and population densities where traditional data is sparse. This method improves population estimates and supports national security by enhancing emergency response capabilities.
Debjani Singh, a senior scientist at ORNL, leads the HydroSource project, which enhances hydropower research by making water data more accessible and useful. With a background in water resources, data science, and earth science, Singh applies innovative tools like AI to advance research. Her career, shaped by her early exposure to science in India, focuses on bridging research with practical applications.
A study found that beaches with manmade fortifications recover more slowly from hurricanes than natural beaches, losing more sand and vegetation. The researchers used satellite images and light detection and ranging data, or LIDAR, to measure elevation changes and vegetation coverage. Changes in elevation showed how much sand was depleted during the storm and how much sand returned throughout the following year.
Joe Tuccillo, a human geography research scientist, leads the UrbanPop project that uses census data to create synthetic populations. Using a Python software suite called Likeness on ORNL’s high-performance computers, Tuccillo’s team generates a population with individual ‘agents’ designed to represent people that interact with other agents, facilities and services in a simulated neighborhood.
The Department of Energy’s Oak Ridge National Laboratory has publicly released a new set of additive manufacturing data that industry and researchers can use to evaluate and improve the quality of 3D-printed components. The breadth of the datasets can significantly boost efforts to verify the quality of additively manufactured parts using only information gathered during printing, without requiring expensive and time-consuming post-production analysis.
As a data scientist, Daniel Adams uses storytelling to parse through a large amount of information to determine which elements are most important, paring down the data to result in the most efficient and accurate data set possible.
Researchers at Oak Ridge National Laboratory have developed free data sets to estimate how much energy any building in the contiguous U.S. will use in 2100. These data sets provide planners a way to anticipate future energy needs as the climate changes.
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
ORNL scientists develop a sample holder that tumbles powdered photochemical materials within a neutron beamline — exposing more of the material to light for increased photo-activation and better photochemistry data capture.