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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Joachim Ehrhardt
Nuclear Technology | Volume 31 | Number 1 | October 1976 | Pages 123-132
Technical Paper | Instrument | doi.org/10.13182/NT76-A31704
Articles are hosted by Taylor and Francis Online.
A new method for the detection of sodium boiling in liquid-metal fast breeder reactors is based on the assumption that the boiling of sodium produces fluctuations of the neutron flux within a restricted frequency range. Accordingly, a resonance-type increase in the power spectral density of neutron noise signals is observed. General criteria relating detection sensitivity, false alarm rate, and response time of a detection system are derived from theoretical considerations. Results are not dependent on the shape of the frequency spectra and are applicable to all noise signals with approximately normally distributed amplitudes. Theoretical formulas were confirmed in a number of experimental parameter studies for the optimal detection of sodium boiling. Computations based on these results predict that local and integral sodium boiling can be detected in a wide core range of the SNR 300 by observing fluctuations of the neutron flux.