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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.
Henk W. Kalfsbeek
Nuclear Technology | Volume 84 | Number 3 | March 1989 | Pages 296-304
Technical Paper | Probabilistic Safety Assessment and Risk Management / Nuclear Safety | doi.org/10.13182/NT89-A34213
Articles are hosted by Taylor and Francis Online.
The Abnormal Occurrences Reporting System (AORS) of the Commission of the European Communities is a data bank that contains homogenized information on safety-related events that have occurred in nuclear power plants in Europe and the United States, covering ∼780 yr of total reactor operation from 1969 to 1985. The use and analysis potential of this information system are illustrated, particularly with respect to the support it gives to incident analysts and probabilistic safety assessment (PSA) practitioners. As an example, attention is focused on a particular methodology for using the incident data during the modeling stage of a PSA study, or more generally, in the course of any type of incident analysis. In this case, a sophisticated multistep retrieval procedure identifies a set of event reports from the data bank, of which the (possible) relationship is unknown a priori, but which are brought together under the attention of the analyst. Seen together within the framework of the study in hand, these reports might yield valuable information for upgrading the completeness of the system, subsystem, and component models in terms of failure modes and effects, fault propagation paths, and unforeseen system interactions. This semiautomatic procedure exploits a feature that renders the AORS unique among international safety-related event data bases, namely, the codification and storage of causal sequences extracted from each event report.