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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.
S. Pearlstein
Nuclear Science and Engineering | Volume 74 | Number 3 | June 1980 | Pages 215-219
Technical Note | doi.org/10.13182/NSE80-A20123
Articles are hosted by Taylor and Francis Online.
The adjustment of differential data can improve the agreement between calculation and experiment of integral quantities, but the adjustment process also introduces a posteriori correlations among the data that were not part of the a priori assumptions. In a forward calculation of integral parameters using adjusted differential data, the a posteriori correlations in general reduce the estimated uncertainty since the linear independence among differential data is reduced but the correlations inhibit the use of integral data to improve individual pieces of differential data. The adjusted data are validated for the calculation of integral parameters similar to those used in the adjustment. The physical interpretation of data adjustments is illustrated using a simple model to analyze bare homogeneous critical assemblies.
