# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 1 Introduction  
Reflooding is a critical recovery process in a reactor following a loss of coolant accident (LOCA). It entails introducing subcooled water from accumulators to cool down overheated fuel rods, mitigating the risk of reactor meltdown--a severe accident in nuclear safety. Despite its significance, the intricacies of the reflooding phase, especially concerning the flow regime and heat transfer, remain challenging to predict [1, 2, 3]. In pressurized water reactors (PWRs), enhancing predictions related to reflooding is crucial as they influence the effectiveness of the emergency core cooling system (ECCS) during a LOCA [4, 5, 6, 7]. An essential aspect of this is the accurate prediction of quenchingtime, as once the fuel rods are quenched, they cool to natural convection levels, averting potential fuel meltdown and ensuring safety. 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]. To enhance the predictions of the system thermal-hydraulics code (TRACE) [18], a combined version of the transient reactor analysis code (TRAC) and the reactor excursion and leak analysis program (RELAP5 [19]), the effects of DFFB and IAFB heat transfers, which usually occur in the reflooding phase in a rod bundle, were examined at the Pennsylvania State University/U.S. Regulatory Commission Rod Bundle Heat Transfer (RBHT) test facility [20]. This test data can enhance the reflood model in the TRACE code. Therefore, the TRACE code's more accurate and reliable reflood model can be realistically achieved rather than the conservative predictions. Simulating the full-length emergency core cooling heat transfer-separate effects tests and system-effects tests (FLECHT SEASET) and RBHT reflood tests, the existing reflood model of the multidimensional analysis of reactor safety (MARS [21]) code, such as the DFFB and IAFB wall heat transfer models, were improved [22]. Moreover, the effectiveness of ECCS is considerably concentrated in the flooding experiment with blocked arrays (FEBA) with the deformed claddings in the fuel assembly [23]. In the FEBA project, some separate effect test series are carefully investigated with various blockage geometries that simulate the ballooned fuel rod claddings. Consequently, the effects of spacer grids and the heat transfer enhancements of the downstream region for ballooned fuel rods with various blockage ratios were evaluated. 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. 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. 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.