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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 ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.
Faults in the power grid cause many problems that can result in catastrophic failures. Real-time fault detection in the power grid system is crucial to sustain the power systems' reliability, stability, and quality.
Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.
This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches.
ORNL has developed a new hydrothermal synthesis route to generate high quality battery cathode precursors. The new route offers excellent compositional control, homogenous spherical morphologies, and an ammonia-free co-precipitation process.
Sodium-ion batteries are a promising candidate to replace lithium-ion batteries for large-scale energy storage system because of their cost and safety benefits.