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Researcher
- Amit Shyam
- Beth L Armstrong
- Peeyush Nandwana
- Ryan Dehoff
- Alex Plotkowski
- Brian Post
- Edgar Lara-Curzio
- Jun Qu
- Rangasayee Kannan
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- Zhili Feng
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- Alice Perrin
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- Bishnu Prasad Thapaliya
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- Ethan Self
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- Govindarajan Muralidharan
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- Marie Romedenne
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- Marm Dixit
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- Priyanshi Agrawal
- Rishi Pillai
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- Sergiy Kalnaus
- Shajjad Chowdhury
- Sunyong Kwon
- Thomas R Muth
- Tim Graening Seibert
- Tolga Aytug
- Trevor Aguirre
- Venugopal K Varma
- Weicheng Zhong
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- Yukinori Yamamoto

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.

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

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 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.

V-Cr-Ti alloys have been proposed as candidate structural materials in fusion reactor blanket concepts with operation temperatures greater than that for reduced activation ferritic martensitic steels (RAFMs).

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.

This invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi