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Researcher
- Andrzej Nycz
- Chris Masuo
- Amit Shyam
- Beth L Armstrong
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- Zhili Feng
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- Dali Wang
- David J Mitchell
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- Ethan Self
- Gabriel Veith
- Gerry Knapp
- Glenn R Romanoski
- Gordon Robertson
- Govindarajan Muralidharan
- J.R. R Matheson
- James Klett
- Jaydeep Karandikar
- Jay Reynolds
- Jeff Brookins
- Jesse Heineman
- Jiheon Jun
- John Potter
- Jordan Wright
- Jovid Rakhmonov
- Khryslyn G Araño
- Loren L Funk
- Marm Dixit
- Matthew S Chambers
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- Nancy Dudney
- Nicholas Richter
- Polad Shikhaliev
- Priyanshi Agrawal
- Riley Wallace
- Ritin Mathews
- Roger G Miller
- Rose Montgomery
- Sarah Graham
- Sergiy Kalnaus
- Shajjad Chowdhury
- Steven J Zinkle
- Sunyong Kwon
- Theodore Visscher
- Thomas R Muth
- Tim Graening Seibert
- Tolga Aytug
- Trevor Aguirre
- Venugopal K Varma
- Vincent Paquit
- Vladimir Orlyanchik
- Vladislav N Sedov
- Weicheng Zhong
- Wei Tang
- William Peter
- Xiang Chen
- Xiaohan Yang
- Yacouba Diawara
- Yanli Wang
- Yiyu Wang
- Yukinori Yamamoto
- Yutai Kato

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.

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.

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

ORNL has developed a large area thermal neutron detector based on 6LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding.

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.

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called