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      SCIE SCOPUS KCI등재

      FIRE PROPAGATION EQUATION FOR THE EXPLICIT IDENTIFICATION OF FIRE SCENARIOS IN A FIRE PSA

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      https://www.riss.kr/link?id=A100955009

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      When performing fire PSA in a nuclear power plant, an event mapping method, using an internal event PSA model, is widely used to reduce the resources used by fire PSA model development. Feasible initiating events and component failure events due to fi...

      When performing fire PSA in a nuclear power plant, an event mapping method, using an internal event PSA model, is widely used to reduce the resources used by fire PSA model development. Feasible initiating events and component failure events due to fire are identified to transform the fault tree (FT) for an internal event PSA into one for a fire PSA using the event mapping method. A surrogate event or damage term method is used to condition the FT of the internal PSA. The surrogate event or the damage term plays the role of flagging whether the system/component in a fire compartment is damaged or not, depending on the fire being initiated from a specified compartment. These methods usually require explicit states of all compartments to be modeled in a fire area. Fire event scenarios, when using explicit identification, such as surrogate or damage terms, have two problems: (1) there is no consideration of multiple fire propagation beyond a single propagation to an adjacent compartment, and (2) there is no consideration of simultaneous fire propagations in which an initiating fire event is propagated to multiple paths simultaneously. The present paper suggests a fire propagation equation to identify all possible fire event scenarios for an explicitly treated fire event scenario in the fire PSA. Also, a method for separating fire events was developed to make all fire events a set of mutually exclusive events, which can facilitate arithmetic summation in fire risk quantification. A simple example is given to confirm the applicability of the present method for a $2{\times}3$ rectangular fire area. Also, a feasible asymptotic approach is discussed to reduce the computational burden for fire risk quantification.

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