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Conference Spotlight
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.
Per G. Reinhall, Kwanhum Park, Robert W. Albrecht
Nuclear Technology | Volume 83 | Number 2 | November 1988 | Pages 197-204
Technical Paper | Nuclear Fuel | doi.org/10.13182/NT88-A34161
Articles are hosted by Taylor and Francis Online.
The next generation of liquid-metal fast breeder reactors will likely be passively safe designs in that the reactors will be able to survive a loss-of-flow transient without relying on active safety devices. Thermal distortion of the core assemblies is envisioned as one of the most important contributors to the passive negative reactivity feedback required to control the reactor. Development of these reactors requires that the shape of the distorted fuel assemblies be accurately predicted. It is common practice to use beam elements in the modeling of thermally distorted fuel assemblies. However, by using higher order finite element analysis, it is found that the accuracy of such beam element models are unsatisfactory and should only be used with caution. The investigation shows that this lack of accuracy can be largely overcome by a modification of the beam elements such that the moments created by the frictional contact forces are taken under consideration. In addition, investigation of the effect of the fuel pin bundle indicates that the thermal distortion of fuel assemblies can be made significantly more accurate by including the commonly neglected fuel pins.