| JOURNAL OF NUCLEAR MATERIALS | 卷:523 |
| Variance-based sensitivity analysis applied to the hydrogen migration and redistribution model in Bison. Part II: Uncertainty quantification and optimization | |
| Article | |
| Aly, Zineb1  Casagranda, Albert2  Pastore, Giovanni2  Brown, Nicholas R.3  | |
| [1] Penn State Univ, University Pk, PA 16802 USA | |
| [2] Idaho Natl Lab, Idaho Falls, ID 83415 USA | |
| [3] Univ Tennessee, Knoxville, TN 37996 USA | |
| 关键词: Hydrides; Fuel performance code analysis; Variance-based sensitivity analysis; Optimization; Bison; Dakota; Sobol; | |
| DOI : 10.1016/j.jnucmat.2019.06.023 | |
| 来源: Elsevier | |
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【 摘 要 】
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. (C) 2019 Elsevier B.V. All rights reserved.
【 授权许可】
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【 预 览 】
| Files | Size | Format | View |
|---|---|---|---|
| 10_1016_j_jnucmat_2019_06_023.pdf | 3855KB |
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