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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.
Estimates based on the U.S. Department of Energy (DOE) test procedure for water heaters indicate that the equivalent of 350 billion kWh worth of hot water is discarded annually through drains, and a large portion of this energy is, in fact, recoverable.
Neutron scattering experiments cover a large temperature range in which experimenters want to test their samples.
The incorporation of low embodied carbon building materials in the enclosure is increasing the fuel load for fire, increasing the demand for fire/flame retardants.
Neutron beams are used around the world to study materials for various purposes.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
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).