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Texas A&M welcomes uranium conversion research
The Texas A&M Engineering Experiment Station (TEES) has signed a research agreement with Quantum Leap Energy (QLE) “to advance and de-risk the commercial production of high-purity uranium hexafluoride (UF6).”
QLE is an Austin, Texas–based subsidiary of ASP Isotopes (ASPI), which is developing an isotope enrichment platform for applications in nuclear energy, nuclear medicine, and semiconductors. QLE specializes in the uranium conversion step of the nuclear fuel cycle—the conversion of yellowcake uranium concentrate (U3O8) into UF6 prior to enrichment.
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.