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ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
We presented a novel apparatus and method for laser beam position detection and pointing stabilization using analog position-sensitive diodes (PSDs).
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.
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.
Neutron scattering experiments cover a large temperature range in which experimenters want to test their samples.
Using all polymer formulations, the PIP densification is improved almost 70% over traditional preceramic polymers and PIP material leading to cost and times saving for densifying ceramic composites made from powder or fibers.
Neutron beams are used around the world to study materials for various purposes.