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The busyness of the nuclear fuel supply chain
Ken Petersenpresident@ans.org
With all that is happening in the industry these days, the nuclear fuel supply chain is still a hot topic. The Russian assault in Ukraine continues to upend the “where” and “how” of attaining nuclear fuel—and it has also motivated U.S. legislators to act.
Two years into the Russian war with Ukraine, things are different. The Inflation Reduction Act was passed in 2022, authorizing $700 million in funding to support production of high-assay low-enriched uranium in the United States. Meanwhile, the Department of Energy this January issued a $500 million request for proposals to stimulate new HALEU production. The Emergency National Security Supplemental Appropriations Act of 2024 includes $2.7 billion in funding for new uranium enrichment production. This funding was diverted from the Civil Nuclear Credits program and will only be released if there is a ban on importing Russian uranium into the United States—which could happen by the time this column is published, as legislation that bans Russian uranium has passed the House as of this writing and is headed for the Senate. Also being considered is legislation that would sanction Russian uranium. Alternatively, the Biden-Harris administration may choose to ban Russian uranium without legislation in order to obtain access to the $2.7 billion in funding.
Nadish Saini, Igor A. Bolotnov
Nuclear Technology | Volume 208 | Number 8 | August 2022 | Pages 1244-1265
Technical Paper | doi.org/10.1080/00295450.2021.1974279
Articles are hosted by Taylor and Francis Online.
Spacer grids and mixing vanes exhibit a significant role in the thermal hydraulics of pressurized water reactors (PWRs), especially in the post loss-of-coolant accident regimes. A detailed analysis of the contrasting upstream and downstream turbulent flow features is of great importance to both system codes and computational fluid dynamics (CFD)–Reynolds-averaged Navier–Stokes (RANS) modeling. Further, with the advent of supercomputing resources and machine learning research, a data-driven approach to turbulence modeling is gaining popularity. However, owing to the complexities associated with large-scale, high-fidelity data collection capabilities, the application of machine learning–based turbulence models has been limited to simple geometries. In this work, using a highly scalable CFD code PHASTA, we have collected data from direct numerical simulations of a PWR subchannel with high spatial and temporal resolution. From the collected data we extract key turbulent flow features, including mean velocities and Reynolds stresses that highlight the effects of spacer grids and mixing vanes on downstream turbulence in a typical PWR subchannel. An invariant analysis of the anisotropic stress tensor is also presented, which further elucidates their effect on the nature of turbulence in the immediate upstream and downstream vicinity. The high-resolution data from the simulations are archived and intended for the development of data-driven RANS closure models that are capable of capturing the evolution of anisotropy in PWR subchannels.