To advance emergency response (ER) modeling in probabilistic risk assessment (PRA), this research offers a new methodology that explicitly incorporates the spatiotemporal evolution of underlying physical and social phenomena and their bidirectional interactions. While this methodology is applicable for various ER scenarios on different spatial and temporal scales, this paper focuses on advancing ER modeling for a nuclear power plant (NPP) internal fire. This paper provides a thorough review and categorization of existing studies on internal fire ER modeling for NPPs and highlights the contributions of this research. This paper then develops a new methodology for fire ER modeling by integrating an agent-based model of first responder performance (FRP) with a fire hazard propagation (FHP) model through a shared geographical information system (GIS)-based spatial simulation environment. This research is the first to explicitly incorporate space (in addition to time) into the FRP modeling within ER modeling of NPP fire probabilistic risk assessment (fire PRA). In addition, this research develops a GIS-based interface between FRP and FHP that has the capability of transferring both spatial and temporal information in a bidirectional way. Although this paper is focused on a fire ER scenario, the new methodology developed in this paper can contribute to modeling external control room (ExCR) human performance in other contexts, such as diverse and flexible coping strategy (FLEX), maintenance, and offsite first responders in level 3 PRA.
Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools, and data for modeling this risk are highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data's high rate of uncertainty. This report outlines work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant PRA models. This research used the Integrated Probabilistic Risk Assessment (I-PRA) method and paves the way to correctly assess and reduce data uncertainty in fire modeling.
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