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Researcher
- Ryan Dehoff
- Edgar Lara-Curzio
- Venkatakrishnan Singanallur Vaidyanathan
- Ying Yang
- Yong Chae Lim
- Zhili Feng
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- Amir K Ziabari
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- Rangasayee Kannan
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- Wei Zhang
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- Yutai Kato
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- Gina Accawi
- Gurneesh Jatana
- Jiheon Jun
- John Holliman II
- Marie Romedenne
- Mark M Root
- Meghan Lamm
- Nidia Gallego
- Nolan Hayes
- Obaid Rahman
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Peter Wang
- Priyanshi Agrawal
- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Shajjad Chowdhury
- Sudarsanam Babu
- Tim Graening Seibert
- Tolga Aytug
- Tomas Grejtak
- Weicheng Zhong
- Wei Tang
- William Peter
- Xiang Chen
- Yan-Ru Lin
- Yiyu Wang
- 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.

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).

A novel method that prevents detachment of an optical fiber from a metal/alloy tube and allows strain measurement up to higher temperatures, about 800 C has been developed. Standard commercial adhesives typically only survive up to about 400 C.

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

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

The microreactor design addresses the need to understand molten salt-assisted electrochemical processes at a controlled scale, enabling real-time observation of structural changes and kinetics.

With the ever-growing reliance on batteries, the need for the chemicals and materials to produce these batteries is also growing accordingly. One area of critical concern is the need for high quality graphite to ensure adequate energy storage capacity and battery stability.