Analysis of the effect of liquid droplet models on the reflood heat transfer using the CUPID code

Yoo, Ju Soon; Cho, Yun Je; Yoon, Han Young; Jeong, Jae Jun

doi:10.1016/j.nucengdes.2019.110148

Cited by 13 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 11 illustrates the most influential physical model STD analysis, specifically for the C38, corresponding to the SFB heat transfer model in Table 2. This result is consistent with the outcome of previous study [25,26,40], indicating that the film boiling heat transfer is the most crucial physical model in the reflooding phenomena simulation. In addition, the interfacial friction factor for inverted annular flow, slug flow, and interphase heat transfer coefficients for stratified and annular to stratified transition also strongly contribute to this UQ analysis using STD evaluation (see Figure 11).…”

Section: Sensitive Physical Model Investigation Resultssupporting

confidence: 93%

Machine Learning Applications and Uncertainty Quantification Analysis for Reflood Tests

Tiep,

Kim,

Jeong

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Sensitive Physical Model Investigation Resultssupporting

confidence: 93%