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A probabilistic safety assessment (PSA) is used to estimate potential risks associated with a nuclear power plant. Event trees (ETs) and fault trees (FTs) describe accident scenarios following initiating events. They allow for identifying and quantifying the consequence frequencies. Based on success criteria, support studies are used to determine each scenario’s consequence. They are also used to define the time available for the operators to carry out the actions involved in the scenario, and consequently, the corresponding human failure probability. In this paper, we use a dynamic PSA toolbox to optimize support studies for a classical (ET/FT) PSA model. We analyze medium-break loss-of-coolant accident (LOCA) initiating events with the possible failure of the high-pressure safety injection (HPSI) system for an internal event Level 1 PSA for a pressurized water reactor plant. In order to prevent core uncovery and subsequent core damage, operators must depressurize the primary circuit to reach the low-pressure safety injection set point. This scenario represents a significant contribution to the core damage frequency. The dominant cut sets corresponding to this scenario involve HPSI failure to run over 24 h, while the thermal-hydraulic analysis supporting the human reliability assessment (HRA) analysis for this sequence assumes the unavailability of HPSI pumps at the beginning of the accident. We update the thermal-hydraulic assumptions in the support study by performing simulations for different values of the HPSI failure time. We find that even a short HPSI operation time buys significant (from the HRA point of view) available time for operators and drives significant improvement in estimating the human error probability (HEP). We postprocessed the minimal cut-set probability by integrating the obtained HEP. This result allows for more realistic quantification of the contribution of the medium-break LOCA in the core damage frequency.

A Pragmatic Approach to Update Support Studies and Human Reliability Analyses for a PSA Model