*6.6.2. Results*

Different ranges of conditional explosion-probability PE are depicted in Figure 14. The denotation of the different ranges of the explosion event probability PE, which is normalised on 1m2, is as follows: red area (> 1E-07/m2), orange area (≤ 1E-07/m2), yellow area (≤ 5E-08/m2), green area (≤ 1E-08/m2). The methods, number of trials, the simulation time and the results like mean value, variance and figure of merit (fom) are listed in Table 6.

Probabilistic Assessment of Nuclear Power Plant Protection Against External Explosions 147

The results of the MCS are evaluated on the condition that the accident already occurred. In order to assess the frequency of occurrence of an external explosion event the frequency of accidents with combustible gas has to be considered. It should be noticed that the results for the frequency of occurrence of an external explosion event will be several magnitudes lower than the results for the conditional explosion event probability given in this paper. Furthermore 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

Figures 10, 12, and 14 indicate that the conditional explosion-frequency decreases as the distance to the place of accident increases. This is due to the exponentially distributed

The results in Tables 4, 5 and 6 indicate that the conditional probability of occurrence of external explosion pressure waves in consideration of realistic conditions (accident frequency depending on environmental conditions, wind direction & wind speed) can be

With the aid of biasing techniques the MCS becomes more efficient, the variance is reduced and the figure of merit (fom) rises. In most cases it can be found out that the solutions fit approx. the same mean 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

**7. Countermeasures to avoid or mitigate the adverse effects of external** 

Knowledge of the explosion characteristics and the structural impact on buildings of the respective plant is necessary to determine the appropriate countermeasures in order to ensure a safe operation of the nuclear power plant. However, fundamental changes of the plant under consideration are mainly possible only during the design and construction

Basic features of the loads induced on structures by air blasts are described in IAEA Design Guide [5] in terms of a normalized distance that takes into account the amount and type of the explosive charge. The guide presents charts that allow the determination of the peak value of the incident pressure, the total impulse of the positive phase and other relevant design parameters, which are generally used for design or verification purposes of sensitive nuclear structures. For the determination of the resulting actions on structures subjected to a specified blast event, the load-time functions induced by the incident pressure wave must be

In general, it is impossible to protect structures from all man-made and natural hazards. However, assessing the possible damages caused by a defined hazard enables risk-informed

ignition probability which depends on the time or the distance to the accident.

combination with the ffe and biasing techniques is the most efficient approach.

**6.7. Summary of results** 

any specific application.

**explosions** 

evaluated in the next step.

phase.

successfully assessed by means of the MCS.


**Figure 14.** Gas-tanker accident - ranges of conditional explosion event probability PE/1m2

**Table 6.** Gas-tanker accident - conditional probability of an explosion event within the plant area with radius rP

Close to the river-sections 2 and 3 the conditional explosion event probability increases, this is due to the higher accident frequency in these sections combined with the specific winddirection frequencies.

As the different Monte Carlo methods (Table 6) are compared it can be found out that most solutions fit a mean about approx. 1.3E-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 Monte Carlo simulations in combination with the ffe and biasing techniques is the most efficient approach.
