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Regional Basic Professional Training Course in Korea

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<strong>Regional</strong> <strong>Basic</strong> <strong>Professional</strong> <strong>Tra<strong>in</strong><strong>in</strong>g</strong> <strong>Course</strong> (BPTC) on Nuclear Safety<br />

9.1.7. Uncerta<strong>in</strong>ties<br />

We can dist<strong>in</strong>guish three major categories of sources of uncerta<strong>in</strong>ties <strong>in</strong> the PRA models.<br />

9.1.7.1. Completeness<br />

There is never a guarantee that all the <strong>in</strong>itiat<strong>in</strong>g events lead<strong>in</strong>g to significant<br />

consequences have been taken <strong>in</strong>to account. This lack of completeness <strong>in</strong>troduces an<br />

uncerta<strong>in</strong>ty <strong>in</strong> the results and conclusions of the analysis that is difficult to assess and<br />

quantify.<br />

9.1.7.2. Model<strong>in</strong>g adequacy<br />

There are uncerta<strong>in</strong>ties <strong>in</strong>troduced by the relative <strong>in</strong>adequacy of the conceptual models<br />

and the mathematical models. Sensitivity studies are usually performed to assess their<br />

relative importance. In the PSA level 2, the uncerta<strong>in</strong>ties of the progression of the<br />

accident are so important that it is necessary to use expert judgment to show the results of<br />

the physical progression <strong>in</strong> terms of probability distribution.<br />

9.1.7.3. Input parameter uncerta<strong>in</strong>ties<br />

This category is related to the uncerta<strong>in</strong>ties of the parameters of the models used <strong>in</strong> the<br />

PSA. This type of uncerta<strong>in</strong>ty is more easily quantified.<br />

9.1.7.4. Propagation of uncerta<strong>in</strong>ties<br />

The quantification of the third category of uncerta<strong>in</strong>ties is usually done by us<strong>in</strong>g a<br />

Monte-Carlo simulation that consists of generat<strong>in</strong>g a random sample of <strong>in</strong>puts of the<br />

model determ<strong>in</strong><strong>in</strong>g the PSA output from each set of <strong>in</strong>puts <strong>in</strong> the sample.<br />

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