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
- Chris Tyler
- Justin West
- Ritin Mathews
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- David Olvera Trejo
- Diana E Hun
- J.R. R Matheson
- Jaydeep Karandikar
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Scott Smith
- Soydan Ozcan
- Stephen M Killough
- Vincent Paquit
- Xianhui Zhao
- Akash Jag Prasad
- Alex Roschli
- Brian Gibson
- Brian Post
- Bryan Maldonado Puente
- Calen Kimmell
- Corey Cooke
- Emma Betters
- Erin Webb
- Evin Carter
- Gina Accawi
- Greg Corson
- Gurneesh Jatana
- Halil Tekinalp
- Jeremy Malmstead
- Jesse Heineman
- John Holliman II
- John Potter
- Josh B Harbin
- Kitty K Mccracken
- Mark M Root
- Mengdawn Cheng
- Michael Kirka
- Nolan Hayes
- Obaid Rahman
- Oluwafemi Oyedeji
- Paula Cable-Dunlap
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Sanjita Wasti
- Tony L Schmitz
- Tyler Smith
- Vladimir Orlyanchik

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.

We have developed a novel extrusion-based 3D printing technique that can achieve a resolution of 0.51 mm layer thickness, and catalyst loading of 44% and 90.5% before and after drying, respectively.

System and method for part porosity monitoring of additively manufactured components using machining
In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

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

Distortion generated during additive manufacturing of metallic components affect the build as well as the baseplate geometries. These distortions are significant enough to disqualify components for functional purposes.

For additive manufacturing of large-scale parts, significant distortion can result from residual stresses during deposition and cooling. This can result in part scraps if the final part geometry is not contained in the additively manufactured preform.

The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.

In additive manufacturing large stresses are induced in the build plate and part interface. A result of these stresses are deformations in the build plate and final component.