{"title":"Nuclear Dry Cask Storage Container CFD Model Experimental Validation and Uncertainty Quantification","authors":"Kimbal Hall, A. Zigh, J. Solı́s","doi":"10.1115/vvs2019-5142","DOIUrl":null,"url":null,"abstract":"\n CFD is a valuable tool for showing compliance with peak temperatures in dry cask storage systems (DCSS). When demonstrating compliance, it is valuable to quantify the uncertainty in the simulation result as a function of the computational mesh and simulation inputs. USNRC was a participant in a CFD validation test using the TN-32B cask, with extensive temperature measurements throughout the DCSS, including measurements on the cask surface and in fuel bundles.\n This paper discusses validation and uncertainty quantification of a CFD model using experimental data. Uncertainty quantification follows the procedures outlined in ASME V&V20-2009 [1]. Sources of uncertainty that were examined in the analysis include iterative uncertainty, spacial discretization, and uncertainty due to approximately twenty input parameters. Input parameters investigated include environmental conditions, material properties, decay heat, and the spacing of the many small gaps in the installation. The uncertainty in gap size was found to be a particularly large source of uncertainty in this particular installation.\n Results of a “base case” using the conservative estimates outlined in the updated final safety analysis report (UFSAR) [2] are presented, as well as a “best estimate case” that uses more realistic values. These results are compared to experimentally measured values, which fall within the uncertainty band of the analysis. This work is also the subject of an upcoming NUREG/CR.","PeriodicalId":87343,"journal":{"name":"Proceedings of the ASME Verification and Validation Symposium -2019- : VVS2019 : presented at ASME 2019 Verification and Validation Symposium, May 15-17, 2019, Las Vegas, Nevada, USA. ASME Verification and Validation Symposium (2019 : L...","volume":"86 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ASME Verification and Validation Symposium -2019- : VVS2019 : presented at ASME 2019 Verification and Validation Symposium, May 15-17, 2019, Las Vegas, Nevada, USA. ASME Verification and Validation Symposium (2019 : L...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/vvs2019-5142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
CFD is a valuable tool for showing compliance with peak temperatures in dry cask storage systems (DCSS). When demonstrating compliance, it is valuable to quantify the uncertainty in the simulation result as a function of the computational mesh and simulation inputs. USNRC was a participant in a CFD validation test using the TN-32B cask, with extensive temperature measurements throughout the DCSS, including measurements on the cask surface and in fuel bundles.
This paper discusses validation and uncertainty quantification of a CFD model using experimental data. Uncertainty quantification follows the procedures outlined in ASME V&V20-2009 [1]. Sources of uncertainty that were examined in the analysis include iterative uncertainty, spacial discretization, and uncertainty due to approximately twenty input parameters. Input parameters investigated include environmental conditions, material properties, decay heat, and the spacing of the many small gaps in the installation. The uncertainty in gap size was found to be a particularly large source of uncertainty in this particular installation.
Results of a “base case” using the conservative estimates outlined in the updated final safety analysis report (UFSAR) [2] are presented, as well as a “best estimate case” that uses more realistic values. These results are compared to experimentally measured values, which fall within the uncertainty band of the analysis. This work is also the subject of an upcoming NUREG/CR.