In this paper probabilistic methods are applied to a 1D flow model of the Secondary Air System (SAS) of an industrial gas turbine. An overview of the methods applied and the results which can be provided by the probabilistic analysis is given. The paper especially deals with the problems that arise from the high number of probabilistic input parameters connected with a simulation of the Secondary Air System. To overcome these problems a fast method for the detection of nonlinear dependencies is introduced. For improvement in the development process a numerical test plan of the SAS is used to calculate response surfaces describing the system. This allows improvement of the SAS using the response surfaces instead of Monte Carlo Simulations and therefore results in a significant reduction of required flow calculations during the improvement process towards a more robust design. Furthermore this provides a possibility to judge the effects of changes in the input parameters without additional flow calculations.

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