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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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NERS publishes report on machine learning and microreactors
The University of Michigan’s Department of Nuclear Engineering and Radiological Sciences (NERS) has published a summary of a study on nuclear microreactors and machine learning (ML) that was conducted by researchers from NERS and Idaho National Laboratory. The full paper, “Nuclear Microreactor Transient and Load-Following Control with Deep Reinforcement Learning,” was featured in the July issue of Energy Conversion and Management: X.
Vincent P. Manno, Michael W. Golay
Nuclear Technology | Volume 67 | Number 2 | November 1984 | Pages 302-311
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT84-A33518
Articles are hosted by Taylor and Francis Online.
The principal developmental focus of the LIMIT code is the ability to model hydrogen transport accurately in reactor containments. The program is capable of treating rapid two-phase dominated blowdown transients, slower mixing events in which diffusional transport is important, and lumped or nodal multicompartment analysis. The code’s features include versatile multidimensional geometry options and models of ancillary equipment including solid heat sinks and mass and energy sources. The program is applied to a number of pertinent problems including continuum analysis of a hydrogen/water blowdown, simulation of experimental tests performed at the Battelle-Frankfurt Institute and the Hanford Engineering Development Laboratory, and lumped parameter studies of connected room problems. The code is shown to be capable of accurately treating a wide range of problems with reasonable computational efficiency. The need for even better efficiency, additional equipment submodels, and further validation are the code’s principal limitations.