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)
- Isotope Science and Enrichment Directorate (7)
- National Security Sciences Directorate (20)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (138)
- User Facilities (28)
Researcher
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Diana E Hun
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
- Annetta Burger
- Bryan Maldonado Puente
- Carter Christopher
- Chance C Brown
- Corey Cooke
- Debraj De
- Gautam Malviya Thakur
- Gina Accawi
- Gurneesh Jatana
- Hongbin Sun
- James Gaboardi
- Jesse McGaha
- John Holliman II
- Kevin Sparks
- Liz McBride
- Mark M Root
- Michael Kirka
- Nate See
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Prashant Jain
- Ryan Kerekes
- Sally Ghanem
- Thien D. Nguyen
- Todd Thomas
- Xiuling Nie

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

In nuclear and industrial facilities, fine particles, including radioactive residues—can accumulate on the interior surfaces of ventilation ducts and equipment, posing serious safety and operational risks.

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.

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

A novel approach is presented herein to improve time to onset of natural convection stemming from fuel element porosity during a failure mode of a nuclear reactor.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).

Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.