TY - JOUR
T1 - Variance-based sensitivity analysis applied to the hydrogen migration and redistribution model in Bison. Part II
T2 - Uncertainty quantification and optimization
AU - Aly, Zineb
AU - Casagranda, Albert
AU - Pastore, Giovanni
AU - Brown, Nicholas R.
N1 - Funding Information:
This manuscript has been co-authored by Battelle Energy Alliance, LLC under Contract No. DE-AC07-05ID14517 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.
Funding Information:
The authors would like to thank Dr. Bruce Kammenzind and Prof. Arthur Motta for the discussions on hydrides, Prof. Thomas J. Downar for the discussion on UQ, Dr Laura Swiler for her help on coupling Dakota with Bison and the fruitful discusions on Dakota capabilities and finally Mr. Loic Coyle for the discussions on the computational part of this work. This work was funded by a U.S. Department of Energy Integrated Research Project entitled “Development of a Mechanistic Hydride Behavior Model for Spent Fuel Cladding Storage and Transportation: IRP–FC–1: Modeling of Spent Fuel Cladding in Storage and Transportation Environments.”
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/9
Y1 - 2019/9
N2 - We demonstrate a global sensitivity and uncertainty analysis approach to quantify the impact of uncertainty in the hydrogen migration and redistribution models implemented in the U.S. Department of Energy Office of Nuclear Energy fuel performance code Bison. In this study, we provide a brief description of the physical phenomena studied and the sensitivity analysis methods used. To identify the key parameters related to the hydrogen migration and redistribution model in Bison, we study the impact of the variance of the model parameters on the amount of hydrides formed near the outer surface of the nuclear fuel cladding, where hydrides are more likely to form, under the normal operation conditions of a light water reactor. To quantify the impact of the input variance of the parameters on the output variations, we compute the variance-based indices (Sobol indices) and the Pearson correlation coefficients. The results of this work show that the activation energy for the terminal solid solubility of hydride precipitation, the hydrogen heat of transport and the activation energy for hydrogen diffusivity are the key parameters. An optimized set of these parameters was then determined as an attempt to increase the accuracy of Bison predictions by decreasing the root mean square error of the predictions versus experimental results, using a basin-hopping optimization framework.
AB - We demonstrate a global sensitivity and uncertainty analysis approach to quantify the impact of uncertainty in the hydrogen migration and redistribution models implemented in the U.S. Department of Energy Office of Nuclear Energy fuel performance code Bison. In this study, we provide a brief description of the physical phenomena studied and the sensitivity analysis methods used. To identify the key parameters related to the hydrogen migration and redistribution model in Bison, we study the impact of the variance of the model parameters on the amount of hydrides formed near the outer surface of the nuclear fuel cladding, where hydrides are more likely to form, under the normal operation conditions of a light water reactor. To quantify the impact of the input variance of the parameters on the output variations, we compute the variance-based indices (Sobol indices) and the Pearson correlation coefficients. The results of this work show that the activation energy for the terminal solid solubility of hydride precipitation, the hydrogen heat of transport and the activation energy for hydrogen diffusivity are the key parameters. An optimized set of these parameters was then determined as an attempt to increase the accuracy of Bison predictions by decreasing the root mean square error of the predictions versus experimental results, using a basin-hopping optimization framework.
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U2 - 10.1016/j.jnucmat.2019.06.023
DO - 10.1016/j.jnucmat.2019.06.023
M3 - Article
AN - SCOPUS:85067794497
SN - 0022-3115
VL - 523
SP - 478
EP - 489
JO - Journal of Nuclear Materials
JF - Journal of Nuclear Materials
ER -