*6.5.1. Analysis*

The MCS depends on a sequence of single events:

 Accident (x, y)-coordinate: uniformly-distributed depending on the length and the width of street 1 and street 2 (Table 3).

Probabilistic Assessment of Nuclear Power Plant Protection Against External Explosions 145

As the different Monte Carlo methods (Table 5) are compared it can be found out that most solutions fit a mean about approx. 8.0E-04 which verifies the results as well as the adopted different Monte Carlo algorithms. If the variance and the figure of merit are regarded the MCS in combination with the ffe and biasing techniques is the most efficient approach.

The third case study (Figure 13) deals with a gas-tanker accident on the river. The river is subdivided into 6 subsections; each subsection is characterised by an individual length, width and gas-tanker accident frequency. It is assumed, that the accident frequency is higher in sections with confluences or curves than in straight river-sections. The accident-

The vertical distances between the plant and the river are between 440m (dR-1) and 780m (dR-2). In the given application ships can reach every location at the river. Further relevant

 Empirical-distributed accident probability depending on the subsection of the river (Figure 6). It is assumed, that the accident frequency is higher in sections with

Uniformly-distributed accident-coordinate (xi, yi) on condition that the accident

coordinate (xi, yi) is uniformly distributed depending on the river-section i.

application parameters of Figure 13 are given in Table 2 and Table 3.

**6.6. Case study 3 – gas-tanker accident** 

**Figure 13.** Gas-tanker accident on the river

occurred in the river-section i.

The MCS depends on a sequence of single events:

confluences or curves than in straight river-sections.

 Wind-direction φ: empirical-distributed (Figure 7). Wind-speed vW: empirical-distributed (Figure 8). Time τ to ignition: Exp(0.01 s-1)-distributed.

Development of explosive gas mixture: fixed probability (0.3).

*6.6.1. Analysis* 

