This report presents the forward sensitivity analysis method as a means for quantification of uncertainty in system analysis. The traditional approach to uncertainty quantification is based on a black box approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. This approach requires large number of simulation runs and therefore has high computational cost. Contrary to the black box method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In this approach equations for the propagation of uncertainty are constructed and the sensitivity is solved for as variables in the same simulation. This glass box method can generate similar sensitivity information as the above black box approach with couples of runs to cover a large uncertainty region. Because only small numbers of runs are required, those runs can be done with a high accuracy in space and time ensuring that the uncertainty of the physical model is being measured and not simply the numerical error caused by the coarse discretization.
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