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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.
B. Rocca-Volmerange
Nuclear Science and Engineering | Volume 64 | Number 3 | November 1977 | Pages 779-784
Technical Note | doi.org/10.13182/NSE77-A27107
Articles are hosted by Taylor and Francis Online.
This Note expands on a previously communicated synthetic slowing down model to determine the neutron spectra in fast reactors. Based on a polynomial approximation, the model accuracy increases with the order of the expansion. It is, in fact, a generalization to N terms of the one-term classical slowing down models such as those of Fermi, Wigner, and Greuling-Goertzel. Equivalent to the classical and synthetic expression of our QN model, this Note proposes a determination of a “differential” expression of the model, allowing the calculation of a set of functions approximating the kernel Σs(u′ → u). To be used in reactor codes, the spectrum determination has to he associated to a spatial resolution; the second part of this Note is devoted to the adaptation of the QN method to the collision probability approximation or the calculation of a spatial Green's function, to obtain a flux (r,E). The applications in the isotropic collision approximation can be extended to the linearly anisotropic approximation, and various results that demonstrate the validity of the method are given.