Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (23)
- Computing and Computational Sciences Directorate (35)
- Energy Science and Technology Directorate
(217)
- Fusion and Fission Energy and Science Directorate (21)
- Isotope Science and Enrichment Directorate (6)
- National Security Sciences Directorate (17)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate
(128)
- User Facilities (27)
- (-) Information Technology Services Directorate (2)
Researcher
- Amit Shyam
- Ryan Dehoff
- Alex Plotkowski
- Singanallur Venkatakrishnan
- Amir K Ziabari
- James A Haynes
- Philip Bingham
- Sumit Bahl
- Vincent Paquit
- Adam Stevens
- Alice Perrin
- Andres Marquez Rossy
- Brian Post
- Christopher Fancher
- Dean T Pierce
- Diana E Hun
- Gerry Knapp
- Gina Accawi
- Gordon Robertson
- Gurneesh Jatana
- Jason Jarnagin
- Jay Reynolds
- Jeff Brookins
- Jovid Rakhmonov
- Kevin Spakes
- Lilian V Swann
- Mark M Root
- Mark Provo II
- Michael Kirka
- Nicholas Richter
- Obaid Rahman
- Peeyush Nandwana
- Peter Wang
- Philip Boudreaux
- Rangasayee Kannan
- Rob Root
- Roger G Miller
- Sam Hollifield
- Sarah Graham
- Sudarsanam Babu
- Sunyong Kwon
- William Peter
- Ying Yang
- Yukinori Yamamoto

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.