Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (29)
- Computing and Computational Sciences Directorate (39)
- Energy Science and Technology Directorate
(229)
- Fusion and Fission Energy and Science Directorate (24)
- Information Technology Services Directorate (3)
- National Security Sciences Directorate (20)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (138)
- User Facilities (28)
- (-) Isotope Science and Enrichment Directorate (7)
Researcher
- Vivek Sujan
- Adam Siekmann
- Omer Onar
- Subho Mukherjee
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Diana E Hun
- Erdem Asa
- Isabelle Snyder
- Mike Zach
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
- Andrew F May
- Annetta Burger
- Ben Garrison
- Brad Johnson
- Bruce Moyer
- Bryan Maldonado Puente
- Carter Christopher
- Chance C Brown
- Charlie Cook
- Christopher Hershey
- Corey Cooke
- Craig Blue
- Daniel Rasmussen
- Debjani Pal
- Debraj De
- Gautam Malviya Thakur
- Gina Accawi
- Gurneesh Jatana
- Hsin Wang
- Hyeonsup Lim
- James Gaboardi
- James Klett
- Jeffrey Einkauf
- Jennifer M Pyles
- Jesse McGaha
- John Holliman II
- John Lindahl
- Justin Griswold
- Kevin Sparks
- Kuntal De
- Laetitia H Delmau
- Liz McBride
- Luke Sadergaski
- Mark M Root
- Michael Kirka
- Nedim Cinbiz
- Nolan Hayes
- Obaid Rahman
- Padhraic L Mulligan
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Sandra Davern
- Shajjad Chowdhury
- Todd Thomas
- Tony Beard
- Xiuling Nie

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

How fast is a vehicle traveling? For different reasons, this basic question is of interest to other motorists, insurance companies, law enforcement, traffic planners, and security personnel. Solutions to this measurement problem suffer from a number of constraints.

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

Ruthenium is recovered from used nuclear fuel in an oxidizing environment by depositing the volatile RuO4 species onto a polymeric substrate.

The growing demand for electric vehicles (EVs) has necessitated significant advancements in EV charging technologies to ensure efficient and reliable operation.

The growing demand for renewable energy sources has propelled the development of advanced power conversion systems, particularly in applications involving fuel cells.

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

This invention presents a multiport converter (MPC) based power supply to charge the 12 V and 24 V auxiliary batteries in heavy duty (HD) fuel cell (FC) electric vehicle (EV) power train.

The technologies provide a system and method of needling of veiled AS4 fabric tape.