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- Ahmed Hassen
- Vlastimil Kunc
- Steven Guzorek
- Vipin Kumar
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- Singanallur Venkatakrishnan
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- Merlin Theodore
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- Nadim Hmeidat
- Nolan Hayes
- Obaid Rahman
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- Ryan Ogle
- Sally Ghanem
- Samudra Dasgupta
- Sana Elyas
- Steve Bullock
- Subhabrata Saha
- Sudarsanam Babu
- Thomas Feldhausen
- Xianhui Zhao

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

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

This manufacturing method uses multifunctional materials distributed volumetrically to generate a stiffness-based architecture, where continuous surfaces can be created from flat, rapidly produced geometries.

Through utilizing a two function splice we can increase the splice strength for opposing tows.
Contact:
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

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.

The QVis Quantum Device Circuit Optimization Module gives users the ability to map a circuit to a specific quantum devices based on the device specifications.

This invention introduces a continuous composite forming process that produces large parts with variable cross-sections and shapes, exceeding the size of the forming machine itself.

QVis is a visual analytics tool that helps uncover temporal and multivariate variations in noise properties of quantum devices.