Validation and uncertainty quantification for FEBA, FLECHT–SEASET, and PERICLES tests incorporating multi-scaling effects

Heo, Jaeseok; Kim, Kyung Doo; Lee, Seung-Wook

doi:10.1016/j.anucene.2017.08.033

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Among the complex phenomena during reflooding, saturated film boiling (SFB) [8][9][10], dispersed flow film boiling (DFFB) [11,12], and inverted annular film boiling (IAFB) [13,14] play a pivotal role. Understanding these heat transfer processes is crucial for refining predictions and improving reactor safety analysis [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, using the safety and performance analysis code (SPACE) [24] and parallel computing platform integrated for uncertainty and sensitivity analysis (PAPIRUS), the wall and vapor temperature of the FEBA and FLECHT SEASET uncertainty propagation were also examined [16]. The uncertainty quantification (UQ) analysis concluded that the predicted cladding temperature was displayed a highly nonlinear behavior in the reflood phase, resulting in a high simulation uncertainty and computation cost.…”

Section: Introductionmentioning

confidence: 99%

“…It can be revealed that the DA and UQ analysis for the reflooding simulation involved a significant amount of computation and data analysis due to the highly nonlinear system [15,16,25,26]. However, while traditional prediction methods have offered valuable insights, the advent of machine learning presents an opportunity to harness vast amounts of data, refine our predictive capabilities, and achieve unprecedented levels of accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications and Uncertainty Quantification Analysis for Reflood Tests

Tiep,

Kim,

Jeong

et al. 2023

Applied Sciences

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The reflooding phase, a crucial recovery process after a loss of coolant accident (LOCA) in reactors, involves cooling overheated fuel rods with subcooled water. Its complex nature, notably in its flow regime and heat transfer, makes prediction challenging, resulting in high uncertainty and computation cost. In this study, we utilized the data assimilation (DA) technique to enhance the prediction of reflooding phenomena and subsequently deployed machine learning models to predict the accuracy of the safety and performance analysis code (SPACE) simulation. To generate the dataset for the machine learning model, we employed the sampling method for highly nonlinear system uncertainty analysis (STARU), providing a high-quality dataset for a complex problem such as a reflooding simulation. In this dataset, the physical models were assimilated under their selected uncertainty bands and utilized the effective sampling approach of STARU, generating the high-quality output and efficient enhancement of SPACE predictions. Consequently, the implemented machine learning model can be used to enhance model development and uncertainty quantification (UQ) analysis using the system code.

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