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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.
D. C. Wade, R. G. Bucher
Nuclear Science and Engineering | Volume 64 | Number 2 | October 1977 | Pages 517-538
Technical Paper | doi.org/10.13182/NSE77-A27387
Articles are hosted by Taylor and Francis Online.
Flux weighting for the generation of broad-group cross sections is designed to preserve eigenvalue, flux spectrum, and reaction rates; however, it will not preserve adjoint spectrum and reactivity worths. Bilinear (flux-adjoint) weighting preserves all of the above quantities except reaction rates. Bilinear weighting also makes the eigenvalue of the broad-group calculation less sensitive to distortions of the spectrum away from the fundamental mode over which the cross sections were collapsed than is the case when flux weighting is used. A series of 29- and 11-group numerical tests has been made to assess the size of errors (relative to a fine-group standard) in eigenvalue, reaction rate ratios, isotopic worth components, and spectral shapes resulting from the use of the two energy collapse procedures. The errors in adjoint spectrum and calculated worth of scattering materials are found to be large when flux weighting is used.