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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 have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
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.
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.
Test facilities to evaluate materials compatibility in hydrogen are abundant for high pressure and low temperature (<100C).
Digital twins (DTs) have emerged as essential tools for monitoring, predicting, and optimizing physical systems by using real-time data.
Simulation cloning is a technique in which dynamically cloned simulations’ state spaces differ from their parent simulation due to intervening events.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
The technologies provide a coating method to produce corrosion resistant and electrically conductive coating layer on metallic bipolar plates for hydrogen fuel cell and hydrogen electrolyzer applications.
Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).