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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.
B. I. Spinrad
Nuclear Science and Engineering | Volume 62 | Number 1 | January 1977 | Pages 35-44
Technical Paper | doi.org/10.13182/NSE77-A26937
Articles are hosted by Taylor and Francis Online.
Experimental yields, both independent and cumulative, of fission products from thermal-neutron fission of 235U, were combined with semi-empirical model values for those yields for which experimental data were lacking. Using weights determined from experimental errors, or from a priori estimates for model values (which weights were uniformly lower than those for experimental values), and imposing the constraint that cumulative yield is a sum of independent yields of precursors, a most likely consistent set of yields and their errors was determined. The errors were adjusted upward in all cases for which the inferred consistent yield differed by more than its error from the ENDF/B-IV value. Using these yield errors, the sensitivity of decay power to yield uncertainty was determined both for a fission pulse and for very long, low-flux irradiation.