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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.
Toshikazu Takeda, Kazuo Azekura, Tadahiro Ohnishi
Nuclear Science and Engineering | Volume 62 | Number 4 | April 1977 | Pages 709-715
Technical Paper | doi.org/10.13182/NSE77-A15211
Articles are hosted by Taylor and Francis Online.
An improved response matrix method has been proposed to effectively take into account the anisotropy of neutron angular distributions. The method utilizes a relation between the P0 and P1 components of a neutron angular distribution instead of calculating them independently. Hence the number of unknowns as well as computing time can be kept about the same as in the conventional response matrix method which adopts an isotropic approximation of a neutron angular distribution. The proposed method has been evaluated by applying it to one-dimensional slab and two-dimensional hexagonal systems. The results are quite promising: In comparison with the reference SN calculation, the difference of the neutron multiplication factor and power distribution is within 0.1% Δk/k and 2%, respectively, and furthermore, the computing time is reduced to below one-third.
