No abstract

In nuclear safety, the Best-Estimate (BE) codes may be used in demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, develop a method of uncertainty propagation and chose to apply it to the calculation of the Peak Cladding Temperature (PCT) with CATHARE-2 V2.5 code during a Large Break (LB) LOCA event for ZION, a 4-loop PWR of Westinghouse design. As a general rule the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the SOBOL methodology. This technique requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. Kriging methodology (with simulated annealing optimization) for its construction and the SOBOL methodology for the GSA are used. The paper presents the application of the previously described methodology on a LB-LOCA scenario in ZION reactor, associated with 54 input parameters. The output is the first maximum peak cladding temperature of the fuel. Results show that the methodology could be applied to both high-dimensional complex problems and real nuclear power plant calculations.

This paper presents the execution and the results of a rod ejection benchmark inspired by the UAM benchmarks II-2b: cross sections uncertainties propagation during a rod ejection transient in a mini-core (3x3 PWR-type assemblies), without feedback. Performing transient computations in two groups time-dependent neutron diffusion approximation while starting from a steady-state power of 0.1409 MW, we obtained a mean power peak around 0.25 MW with a standard deviation of, nearly, 1.5%. The timedependent standard deviation curve follows the time-dependent mean power curve and the relative standard deviation, in a percentage of the mean power, increases with the mean power peak value. We found, too, that the first group diffusion coefficient and the first group absorption cross section are, practically, the only contributors to the total power peak variance. Drawbacks and findings from the exercise provide essential input for uncertainty propagation in a further multi-physics simulation of a postulated rod ejection accident.

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.