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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Fusion Science and Technology
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.
K. Matsubara, R. Oguma, M. Kitamura
Nuclear Science and Engineering | Volume 65 | Number 1 | January 1978 | Pages 1-16
Technical Paper | doi.org/10.13182/NSE78-A27121
Articles are hosted by Taylor and Francis Online.
An autoregressive (AR) model with pseudo-random binary sequence (PRBS) test signals was applied to the dynamics of the Japan Power Demonstration Reactor, a boiling water reactor (BWR). The decision of the order of the AR model was based on the Akaike criterion. Multi-input test signals of the PRBS were applied to the steam-flow control valve and the forced circulation pump speed control terminal. Seventeen variables including the instrumented fuel assemblies were observed. The AR model identification facilitated building the BWR dynamics model as a multivariable system. The experiment indicated that the BWR dynamics with rather intensive nonwhite noise interference was effectively represented by the AR model, which was compared with a linear theoretical dynamics model. The results suggested that the identified AR model plays an important role in verifying, modifying, and improving the theoretical dynamics model.