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Nuclear Technology
Article
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Nuclear Technology
Article . 2016 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2016
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Challenging Fuel Cycle Modeling Assumptions: Facility and Time-Step Discretization Effects

Authors: Robert W. Carlsen; Paul P. H. Wilson;

Challenging Fuel Cycle Modeling Assumptions: Facility and Time-Step Discretization Effects

Abstract

Due to the diversity of fuel cycle simulator modeling assumptions, direct comparison and benchmarking can be difficult. In 2012 the Organisation for Economic Co-operation and Development completed a benchmark study that is perhaps the most complete published comparison performed. Despite this, various results from the simulators were often significantly different because of inconsistencies in modeling decisions involving reprocessing strategies, refueling behavior, reactor end-of-life handling, etc. This work identifies and quantifies the effects of selected modeling choices that may sometimes be taken for granted in the fuel cycle simulation domain. Four scenarios are compared using combinations of fleet-based or individually modeled reactors with monthly or quarterly (3-month) time steps. The scenarios approximate a transition from the current U.S. once-through light water reactor fleet to a full sodium fast reactor fuel cycle. The Cyclus fuel cycle simulator's plug-in capability along with its market-like dynamic material routing allow it to be used as a level playing field for comparing the scenarios. When under supply-constraint pressure, the four cases exhibit noticeably different behavior. Fleet-based modeling is more efficient in supply-constrained environments at the expense of losing insight on issues such as realistically suboptimal fuel distribution and challenges in reactor refueling cycle staggering. Finer-grained time steps enable more efficient material use in supply-constrained environments resulting in lower standing inventories of separated Pu. Large simulations with fleet-based reactors run much more quickly than their individual reactor counterparts. Gaining a better understanding of how these and other modeling choices affect fuel cycle dynamics will enable making more deliberate decisions with respect to trade-offs such as computational investment vs. realism.

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Keywords

Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Computer Science - Computational Engineering, Finance, and Science

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average