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A novel approach is presented herein to improve time to onset of natural convection stemming from fuel element porosity during a failure mode of a nuclear reactor.
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).
Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.
The need for accurate temperature measurement in critical environments such as nuclear reactors is paramount for safety and efficiency.