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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.
The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.
High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.
We have developed an aerosol sampling technique to enable collection of trace materials such as actinides in the atmosphere.
Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.