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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.
Karl O. Ott
Nuclear Science and Engineering | Volume 64 | Number 2 | October 1977 | Pages 452-464
Technical Paper | doi.org/10.13182/NSE77-A27382
Articles are hosted by Taylor and Francis Online.
An approach for the description and evaluation of the uncertainties in the predicted consequences of fast breeder reactor accidents is outlined. All uncertainties are viewed as uncertainties of parameters, either of physical parameters or of quantities that appear in the parameterization of phenomenological models. By the incorporation of parameter uncertainties in the simulation of the accident progression, single accident-path scenarios assume the character of “accident spectra.” The progression of accident spectra is found by combining deterministically calculated accident-path results with the probability of the respective set of input parameters. The substantial method development needed for the implementation of the approach is discussed, and the status of the development is briefly reviewed. Typical results are presented for illustration purposes. The possible eventual significance of the approach is indicated.