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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.
H. L. Dodds, Jr., P. F. Pasqua
Nuclear Technology | Volume 8 | Number 3 | March 1970 | Pages 296-301
Paper | Technique | doi.org/10.13182/NT70-A28677
Articles are hosted by Taylor and Francis Online.
The differential fast-neutron flux above 0.5 MeV at one spatial position in the High Flux Isotope Reactor was determined experimentally using several threshold activation detectors. The series expansion technique utilizing the concept of least squares was used to obtain an approximate solution to the set of integral equations which are defined by the experimentally determined activation data. Good agreement was achieved between the integrated flux (i.e., the differential flux integrated above 0.5 MeV) determined in this work and the integrated flux determined independently using one-dimensional, multigroup diffusion theory.