EPJ Nuclear Sci. Technol.
Volume 6, 2020
|Number of page(s)||17|
|Published online||28 October 2020|
High to Low pellet cladding gap heat transfer modeling methodology in an uncertainty quantification framework for a PWR Rod Ejection Accident with best estimate coupling
DEN - Service d’études des réacteurs et des mathématiques appliquées (SERMA) CEA, Université Paris-Saclay,
F-91191 Gif-Sur-Yvette, France
2 Centre de Mathématiques Appliquées École Polytechnique, 91128 Palaiseau Cedex, France
* e-mail: email@example.com
Received in final form: 4 August 2020
Accepted: 6 October 2020
Published online: 28 October 2020
High to Low modeling approaches can alleviate the computationally expensive fuel modeling in nuclear reactor’s transient uncertainty quantification. This is especially the case for Rod Ejection Accident (REA) in Pressurized Water Reactors (PWR) were strong multi-physics interactions occur. In this work, we develop and propose a pellet cladding gap heat transfer (Hgap) High to Low modeling methodology for a PWR REA in an uncertainty quantification framework. The methodology involves the calibration of a simplified Hgap model based on high fidelity simulations with the fuel-thermomechanics code ALCYONE1. The calibrated model is then introduced into the CEA developed CORPUS Best Estimate (BE) multi-physics coupling between APOLLO3® and FLICA4. This creates an Improved Best Estimate (IBE) coupling that is then used for an uncertainty quantification study. The results indicate that with IBE the distance to boiling crisis uncertainty is decreased from 57% to 42%. This is reflected to the decrease of the sensitivity of Hgap. In the BE coupling Hgap was responsible for 50% of the output variance while in IBE it is close to 0. These results show the potential gain of High to Low approaches for Hgap modeling in REA uncertainty analyses.
© G. K. Delipei et al., published by EDP Sciences, 2020
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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