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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.
Makoto Sobajima
Nuclear Science and Engineering | Volume 60 | Number 1 | May 1976 | Pages 10-18
Technical Paper | doi.org/10.13182/NSE76-A26852
Articles are hosted by Taylor and Francis Online.
It was found that the results of the RELAP-3 code, which is one of the typical analytical codes for analysis of the loss-of-coolant accident (LOCA) of light water reactors, do not agree well with the results from the ROSA-I experiments under certain break conditions. It was determined that the discharge coefficient used in the code as a parameter can be correlated with the quality of the discharged fluid and that the calculated liquid mass transient does not always agree with the experimental one when a constant bubble escape velocity is assumed. These difficulties come from the possibility of shortcomings of the model dealing with the LOCA phenomena. An attempt was made to improve these aspects of the code by incorporating the correlation of the discharge coefficient with the quality and with Wilson's experimental interpretation of the bubble velocity and certain assumptions in its application. The results obtained by the modified code are in good agreement with both those from the ROSA-I experiment and experiments at Hitachi Ltd.