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Swiss nuclear power and the case for long-term operation
Designed for 40 years but built to last far longer, Switzerland’s nuclear power plants have all entered long-term operation. Yet age alone says little about safety or performance. Through continuous upgrades, strict regulatory oversight, and extensive aging management, the country’s reactors are being prepared for decades of continued operation, in line with international practice.
Satoshi Gunji, Shouhei Araki, Kenya Suyama
Nuclear Science and Engineering | Volume 197 | Number 8 | August 2023 | Pages 2017-2029
Technical papers from: PHYSOR 2022 | doi.org/10.1080/00295639.2022.2164151
Articles are hosted by Taylor and Francis Online.
The fuel debris generated by the accident at the Tokyo Electric Power Company’s Fukushima Daiichi Nuclear Power Plant is expected to have not only heterogeneous but also nonuniform compositions. Similarly, damaged fuel assemblies remaining in the reactor vessels also have nonuniform configurations due to some missing fuel rods. These nonuniformities may cause changing neutron multiplication factors. The effect of nonuniformities on the neutron multiplication factor is clarified by computations, and the possibility of experimentally validating the computations used for criticality management is being investigated. For this purpose, in this study the criticality effects of several core configurations of a new critical assembly, STACY, of the Japan Atomic Energy Agency with nonuniform arrangements of uranium oxide fuel rods, concrete rods, and stainless steel rods were studied to confirm benchmarking potential. The difference in these arrangements changed the neutron multiplication factor by more than 1 $. We confirmed that changes in local neutron moderation conditions and the clustering of specific components caused this effect. In addition, the feasibility of benchmark experimental cores with nonuniform arrangements is evaluated. If benchmarking of such experiments could be realized, it would help validate calculation codes and develop criticality management methods by machine learning.