Monte Carlo analysis of BWR transients : A study on the Best Estimate Plus Uncertainty methodology within safety analyses at Forsmark 3

Transient analyses at Forsmark nuclear power plant are currently performed using realistic computer models in conjunction with conservative estimates of initial- and boundary conditions. This is known as the combined methodology. Due to the conservative estimates, the methodology runs the risk of sometimes over-estimating certain safety criteria which will negatively affect the optimization of reactor operation. The Best Estimate Plus Uncertainty (BEPU) methodology can provide higher safety margins by using probabilities instead of conservatisms when estimating initial and boundary conditions. The BEPU methodology applies a Monte Carlo method to assess the distribution of one or several key outputs. This study focuses on the lowest dryout margin achieved during each Monte Carlo simulation. The tolerance limits of the output are set with the help of Wilks formula using a one-sided 95% tolerance limit with 95% confidence. A total of 36 unique parameters describing initial and boundary conditions have been sampled for each Monte Carlo simulation. The parameters have been sampled using either Gaussian or Uniform distribution functions. The random nature of the Monte Carlo simulations has uncovered alternative event sequences and end states that are not seen in the combined methodology. Assessing the choice of order statistic in Wilks formula also concludes that there are diminishing returns the higher the order statistic is. When choosing the order statistic, one should consider the trade-off between an increased accuracy in the estimated outputs and the increased computational time required. The conservative methodology uses a mix of conservative and nominal estimations of key parameters. The difference in dryout margin between the conservative and the Monte Carlo results should therefore not be used to draw a conclusion about which methodology out-performs the other. While the Monte Carlo simulations do not result in an improved core optimization, they can act as a complement to the combined methodology by providing a more detailed analysis of possible event pathways for a postulated transient.