EPJ Nuclear Sci. Technol.
Volume 7, 2021
Fuel Cycle Simulation TWoFCS 2021
|Number of page(s)||15|
|Published online||21 December 2021|
Application of sensitivity analysis in DYMOND/Dakota to fuel cycle transition scenarios
Argonne National Laboratory,
Lemont, United States
* e-mail: firstname.lastname@example.org
Received in final form: 29 July 2021
Accepted: 18 November 2021
Published online: 21 December 2021
The ability to perform sensitivity analysis has been enabled for the nuclear fuel cycle simulator DYMOND through its coupling with the design and analysis toolkit Dakota. To test and demonstrate these new capabilities, a transition scenario and multi-parameter study were devised. The transition scenario represents a partial transition from the US nuclear fleet to a closed fuel cycle with small modular LWRs and fast reactors fueled by reprocessed used nuclear fuel. Four uncertain parameters in this transition were studied – start date of reprocessing, total reprocessing capacity, the nuclear energy demand growth, and the rate at which the fast reactors are deployed – with respect to their impact on four response metrics. The responses – total natural uranium consumed, maximum annual enrichment capacity required, total disposed mass, and total cost of the nuclear fuel cycle – were chosen based on measures known to be of interest in transition scenarios  and to be significantly impacted by the varying parameters. Analysis of this study was performed both from the direct sampling and through surrogate models developed in Dakota to calculate the global sensitivity measures Sobol’ indices. This example application of this new capability showed that the most consequential parameter to most metrics was the share of new build capacity that is fast reactors. However, for the cost metric, the scaling factor of the energy demand growth was significant and had synergistic behavior with the fast reactor new build share.
© S. Richards and B. Feng, Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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