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Conference Spotlight
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.
Louis Baker, Jr., Richard E. Faw, Francis A. Kulacki
Nuclear Science and Engineering | Volume 61 | Number 2 | October 1976 | Pages 222-230
Technical Paper | doi.org/10.13182/NSE76-A27355
Articles are hosted by Taylor and Francis Online.
Correlations of experimental data on heat transfer from nonboiling, horizontal fluid layers with internal heat generation have been cast into a form suitable for analysis of postaccident heat removal in fast reactors. Available data on layers with equal boundary temperatures indicate that the downward heat transfer rate can be accounted for by conduction alone, while the upward heat transfer rate is largely controlled by convection. A new correlation is presented that applies to evaluation of upward and downward heat fluxes from an internally heated layer with unequal boundary temperatures. Analysis techniques are illustrated in an example calculation pertaining to layers of molten mixed-oxide fuel.