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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.
Erik Johansson
Nuclear Technology | Volume 68 | Number 2 | February 1985 | Pages 263-268
Technical Note | Fabrication of Components of the Creys-Malville Plant / Fission Reactor | doi.org/10.13182/NT85-A33559
Articles are hosted by Taylor and Francis Online.
The recycling of plutonium in close-packed pressurized water reactor (PWR) lattices, leading to a higher conversion ratio than recycling in a normal lattice, has been studied by calculations. These calculations were performed with the multigroup cell and assembly transport theory code CASMO. This code, widely used for normal light water reactor (LWR) lattices, was tested for close-packed ones by calculations on experiments. The outcome of these tests was reasonably good for the parameters of greatest importance in close-packed plutonium-recycle lattices. Subsequently, the code was applied to an LWR system containing PWRs with such lattices. The emphasis in this application was on the net consumption of natural uranium and separative work. In an asymptotic (steady-state) situation for the close-packed lattice case, these amounts turned out to be ∼35% below the corresponding ones for plutonium recycling in a normal lattice.