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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.
Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.
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.
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.
Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.