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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.
John G. Kollas and Allan F. Henry
Nuclear Science and Engineering | Volume 60 | Number 4 | August 1976 | Pages 464-471
Technical Note | doi.org/10.13182/NSE76-A26907
Articles are hosted by Taylor and Francis Online.
A method for obtaining homogenized group-diffusion-theory parameters for heterogeneous nodes (fuel regions plus control elements plus structure) in slab geometry is described. The parameters obtained reproduce exactly the neutron leakage and the integrated reaction rates of the node when it becomes part of a reactor. However, for asymmetric nodes they depend on the fluxes and currents at the surfaces of the node. The sensitivity of this dependence is examined for a one-group model, and numerical examples are given to illustrate that for symmetric nodes, the equivalent parameters are indeed exact and independent of conditions outside the node. For unsymmetric nodes, it is shown that using a set of parameters that is an average of two sets of “exact parameters,” one appropriate to large values of current-to-flux ratio at one of the nodal surfaces and the other appropriate to small values, still yields quite accurate results. For both cases comparisons with results obtained using standard flux-weighted parameters are made.