Abstract

The development of jet engines is typically accompanied by measurement campaigns to mitigate technical risks at an early design stage and to validate the specified performance. Often different configurations are compared in order to determine the superior one. But due to uncertainties in boundary conditions, geometry, and sensors, the derived performance values are subject to uncertainty as well. Assuming that the desired difference in performance metrics is small compared to the associated uncertainty, it can consequently be hard to conclude on the superiority of the configurations.

In this paper, a probabilistic approach is used to estimate the uncertainties of the output values of interest. For that, the measurement process is executed virtually taking the low-speed research compressor of Technische Universität Dresden as a test case. In the first step, a sampling for the uncertainties associated with boundary conditions and geometry is created. Then, Computational Fluid Dynamics (CFD) calculations for these samples are executed. Afterwards, virtual measurements are performed, for which the unbiased value of the measurement quantity of interest is extracted from the CFD calculation and then merged with a measurement uncertainty. Finally, these virtual measurements are evaluated in the same way as it is done for real measurements to derive the output values.

Evaluating the samples allows to obtain the associated uncertainties and hence the confidence levels of a measurement campaign which are to be optimized. The system behavior is first approximated with an inner meta model. This surrogate can be used to create an outer meta model describing the relationship between the variance of the input variables and the uncertainties of the output variables. By evaluating the outer meta model it is possible to identify important input variables effecting the uncertainty of the output variables. Finally, the outer meta model is used to find combinations of input variances, which on one hand lead to the desired output uncertainties and on the other hand are optimal with respect to the associated cost.

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