**6.2. Application**

The following application is a case study that represents the evaluation of the probability of occurrence of an external explosion pressure wave that takes place near a plant. The probability of occurrence is assessed on the condition that an accident with combustible gas already occurred.

Probabilistic Assessment of Nuclear Power Plant Protection Against External Explosions 139

Although the application is described in a generalized way, it incorporates several elements that are typical in order to assess the impact of explosion pressure waves: accident, wind

In the following the example is subdivided into three parts described in sections 6.4 to 6.6: accident at plant-2 (gas holder), accident on street 1 or 2, accident on the river. For each

The probability of an explosion event within the plant area with radius rP is evaluated by means of the Monte Carlo simulation (MCS). In order to make the MCS more efficient biasing techniques are adopted as shown in [26-28]. The algorithm to model and solve the problem is based on the German probabilistic PSA guideline [2] and the supporting

It should be noticed that the events, boundary conditions, parameters and results given in Figures 5 to 14 and Tables 2 to 6 are only example values and do not represent conditions and results of any specific application. However, the described approach is applicable without any general changes by using explicit site and plant specific

example application the frequency of explosions within the radius rP is determined.

direction, wind speed and ignition.

technical document on PSA methods [3].

The case study depends on the following assumptions:

**Figure 6.** Empirical accident river-section frequencies

data.

**6.3. Assumptions**

The application is not restricted to a special field of industry; plants of process industry might be in the focus as well as nuclear power plants. The application is depicted in Figure 5. It consists of plant-1 (in the focus of this study), plant-2 (gasholder e.g.), street 1 and 2 (frequented by tank-lorries that carry explosive liquids) and a river (frequented by gas-tanker that carry explosive liquids). The river is subdivided into 6 subsections; each subsection is characterised by an individual length, width and gas-tanker accident frequency.

**Figure 5.** Case study: plant-1, plant-2, river, road and hazardous scenario (gas-tanker accident)

An accident (plant-2, street 1, street 2 or river) at the coordinate (xi, yi) may cause the development of explosive gas mixture (gas-tanker accident e.g. - Figure 5).

Depending on the wind direction φi the cloud of gas mixture can drift to the plant. An ignition of the gas mixture close to plant-1 (within the radius rP) is in the focus of this study. All relevant application parameters of Figure 5 are given in Table 2.

**Table 2.** Relevant application parameters

Although the application is described in a generalized way, it incorporates several elements that are typical in order to assess the impact of explosion pressure waves: accident, wind direction, wind speed and ignition.

In the following the example is subdivided into three parts described in sections 6.4 to 6.6: accident at plant-2 (gas holder), accident on street 1 or 2, accident on the river. For each example application the frequency of explosions within the radius rP is determined.

The probability of an explosion event within the plant area with radius rP is evaluated by means of the Monte Carlo simulation (MCS). In order to make the MCS more efficient biasing techniques are adopted as shown in [26-28]. The algorithm to model and solve the problem is based on the German probabilistic PSA guideline [2] and the supporting technical document on PSA methods [3].

It should be noticed that the events, boundary conditions, parameters and results given in Figures 5 to 14 and Tables 2 to 6 are only example values and do not represent conditions and results of any specific application. However, the described approach is applicable without any general changes by using explicit site and plant specific data.