Development of the APSRA+ Methodology for Passive System Reliability Analysis and Its Application to the Passive Isolation Condenser System of an Advanced Reactor

摘要

Research in the field of passive system reliability analysis is garnering sharp interest in the nuclear community. Passive systems are being utilized extensively in current- and future-generation reactors for their normal operations as well as for safety critical operations during any accidental conditions. In this paper, we present a methodology called Analysis of Passive System ReliAbility Plus (APSRA~+)for evaluating reliability of passive systems. This methodology is an improved version of the existing APSRA methodology. The methodology has been applied to the passive isolation condenser system (ICS) of the AHWR (Advanced Heavy Water Reactor). With the help of the APSRA~+ methodology, the probability of the passive ICS failing to maintain the clad temperature under 400℃ is estimated to be of the order 1 × 10~(-10). Important features of APSRA~+ are the following. First, it provides an integrated dynamic reliability method for the consistent treatment of dynamic failure characteristics such as multistate failure, fault increment, and time-dependent failure rate of components of passive systems. Second, this methodology overcomes the issue of process parameter treatment by just the probability density function or by root cause analysis, by segregating the parameters into dependent and independent process parameters and then giving a proper treatment to each of them separately. Third, the methodology treats the model uncertainties and independent process parameter variations in a consistent manner. In APSRA~+, the important parameters affecting the passive system under consideration are identified using sensitivity analysis. To evaluate the system performance, a best-estimate system code is used with due consideration of the uncertainties in empirical models. A failure surface is generated by varying all the identified important parameters; variation from the nominal values of these parameters affects the system performance significantly. These parameters are then segregated into dependent and independent categories. For dependent parameters, it is attributed that the variations of process parameters are mainly due to malfunction of mechanical components or control systems, and hence, root cause analysis is performed. The probability of these dependent parameter variations is estimated using a dynamic reliability methodology based on Monte Carlo simulation. The dynamic failure characteristics of the identified causal component/system are accounted for in calculating these probabilities. For the treatment of independent process parameters, using APSRA~+ suggests adopting and integrating classical data-fitting techniques or mathematical models. In the next steps, a response surface-based metamodel is formulated using the generated failure points. The probability of the system being in the failure zone is estimated by sampling and analyzing a sufficiently large number of samples for all the dependent and independent process parameters based on the probability of variations of these parameters, which were estimated using dynamic reliability methodology.