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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Latest News
Princeton-led team develops AI for fusion plasma monitoring
A new AI software tool for monitoring and controlling the plasma inside nuclear fuel systems has been developed by an international collaboration of scientists from Princeton University, Princeton Plasma Physics Laboratory (PPPL), Chung-Ang University, Columbia University, and Seoul National University. The software, which the researchers call Diag2Diag, is described in the paper, “Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas,” published in Nature Communications.
Tai-Ping Lung and Lawrence Ruby
Nuclear Science and Engineering | Volume 59 | Number 4 | April 1976 | Pages 436-440
Technical Note | doi.org/10.13182/NSE76-A26844
Articles are hosted by Taylor and Francis Online.
A generalization of Pacilio's method has been developed for utilizing two detectors to investigate reactor noise. The method requires only the measurement of the polarity of the detector output-current as compared with its average value, and therefore is of use in reactor systems with appreciable subcritical power. The modification extends the theory to the case in which the covariance can exceed unity. A unique bivariate negative binomial distribution is proposed for describing the correlated detectors, and a formula for the covariance-to-mean ratio is developed in terms of the polarity correlations between the detectors. From the ratio, the subcritical reactivity of the system can be determined.