ESTIMATION OF UNCERTAINTY IN DYNAMIC SIMULATION RESULTS

This paper presents a new approach for calculation of uncertainty in d ynamic simulation results. The statistical moments (mean, variance, sk ewness etc.) of the simulation results are calculated using Gaussian-q uadrature with ''customized'' weight function. Based on these moments, an approximating probability density function (pdf) is created by exp ansion into orthogonal polynomial series. The percentiles of the distr ibution can then be calculated. The method is computationally less dem anding than Monte-Carlo simulation when the number of uncertain parame ters are limited. A number of examples are used to illustrate the appl icability of the proposed framework.