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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.
Wasim Raza, Kwang-Yong Kim
Nuclear Technology | Volume 162 | Number 1 | April 2008 | Pages 45-52
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT08-A3932
Articles are hosted by Taylor and Francis Online.
A hybrid multiobjective evolutionary approach to the design optimization of a seven-pin wire-wrapped fuel assembly is applied to achieve an acceptable compromise between two conflicting objectives: enhancement of heat transfer and reduction of pressure drop. Two nondimensional variables, the ratio of wire-spacer diameter to fuel rod diameter and the ratio of wire-wrap pitch to fuel rod diameter, are chosen as design variables. The Latin hypercube sampling method is used to determine the training points. The response surface method is used to approximate the Pareto-optimal front with Reynolds-averaged Navier-Stokes analysis of the flow and heat transfer. The shear stress transport turbulence model is used as turbulence closure. The optimization results are processed by the Pareto-optimal method. The Pareto-optimal solutions are obtained using a combination of the evolutionary algorithm NSGA-II and a local search method. The Pareto-optimal front for the wire-wrapped fuel assembly has been obtained. Six optimal values of the design variables have been obtained using clustering. With the increase in the wire-spacer diameter, both heat transfer and pressure drop in the assembly increase. Increasing the wire-wrap pitch reduces the pressure drop in the assembly at the cost of heat transfer.