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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.
Tsutomu Hoshino
Nuclear Technology | Volume 39 | Number 1 | June 1978 | Pages 46-62
Nuclear Safety Analysis | Energy Modeling and Forecasting / Fuel Cycle | doi.org/10.13182/NT78-A17007
Articles are hosted by Taylor and Francis Online.
A planning tool for strategic operation of nuclear power plants has been presented with a wider view on the overall utility system management. The tool was flexible enough to be capable of checking the feasibility of the proposed alternative plans as well as optimizing the plans in terms of the minimization of system operating costs over several refueling cycles. The problem was defined in a small-scale utility system that consisted of a nuclear power plant and a replacement power station. The optimum decision was made on an in-core refueling pattern, its associated number of fuel assemblies, and the time length of coastdown operation. The optimization was subject to several physical and engineering constraints on reactor operation. Following the general decomposition approach, the method utilized iterative linear programming and a gradient projection algorithm of nonlinear programming. A typical pressurized water reactor was studied. The economic gain was obtained mainly by filling margins originally involved in the reactivity and burnup limitations as well as by optimum coastdown operations. The flexibility of the method was especially enhanced in a case of recovery planning after unexpected plant outages with subsequent forced power reductions.