The intended operating point of turbomachinery is subject to numerous kinds of uncertainty. These range from varying ambient conditions, across geometric deviations in a component, to system related loading variability resulting in engine-to-engine variation in component matching. In order to guarantee safe operation at all conditions, it is essential to consider the above uncertainties when designing turbomachinery.

In the present work, a probabilistic assessment is performed of the influence of possible operational uncertainties on the aerodynamic performance metrics of an aero-engine multistage high pressure compressor (HPC). To propagate uncertainties, Monte Carlo simulations (MCS) with Latin Hypercube Sampling (LHS) were performed, with both correlated and uncorrelated inputs. Each sample consisted of a steady state computational fluid dynamics (CFD) evaluation of the compressor. The statistical input for the boundary conditions was acquired from a MCS of the engine cycle performance at cruise, accounting for flight-to-flight variations in ambient conditions and engine-to-engine variations in component properties.

With the chosen approach, it is possible to quantify the variability in aerodynamic performance of an HPC that is subject to uncertain operating conditions and thus shows the importance of input correlations. Results highlight that deterministically determined performance metrics can differ considerably from the statistical mean, revealing the benefits of a probabilistic assessment. In contrast to performing MCS on the cycle only, a CFD based assessment can also be used to draw conclusions on the aerodynamic mechanisms responsible for changes in efficiency or surge margin.

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