Improvement in the accuracy of SPACE prediction for the unblocked FLECHT SEASET reflood tests by data assimilation

Tiep, Nguyen Huu; Kim, Kyung Doo; Heo, Jaeseok

doi:10.1016/j.anucene.2021.108462

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…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%

“…The UQ analysis for FLECHT SEASET refloods tests was established using the PA-PIRUS framework and SPACE [25], revealing a slight improvement in the simulation and high computation costs. Subsequently, STARU [26] was developed to examine and enhance the UQ for nonindependent and high-dimension parameters, resulting in a highly nonlinear relationship between the physical models and their system states.…”

Section: Uq Analysismentioning

confidence: 99%

“…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. Likewise, the UQ analysis was examined using Markov chain Monte Carlo sampling within the data assimilation (DA) technique of PAPIRUS and SPACE to simulate the FLECHT SEASET reflood tests [25]. It can be inferred that the SFB in the convective heat transfer was identified as the most influential physical model in this DA result after eight months of calculation, indicating a very high 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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Section: Sensitive Physical Model Investigation Resultssupporting

confidence: 93%

Section: Uq Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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