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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.
Ross C. Anderson, Kerry L. Basehore
Nuclear Technology | Volume 77 | Number 2 | May 1987 | Pages 227-230
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT87-A33987
Articles are hosted by Taylor and Francis Online.
A Monte Carlo departure from nucleate boiling ratio (DNBR) limit analysis has recently been performed with a six-channel thermal-hydraulics code model as the tool for DNBR calculations. Compared is the use of the few-channel, fast-running code models with the use of a response surface model as the DNBR calculator. The latter had been used to date for Monte Carlo analyses. The principles of Monte Carlo DNBR limit determination are reviewed to provide the necessary background information.