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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.

Probabilistic design approaches taking geometric variation due to manufacturing scatter or wear into account reflect a relevant part of today’s design process in turbomachinery. This process is characterized by a high degree of multidisciplinarity involving complex geometric features, which demands high standards to incorporate geometric variability. The significance of the employed probabilistic approaches considering real geometry effects is based on the information about the existing variability including distributions, correlations as well as the respective uncertainty of employed measurement techniques. Within the scope of mechanical probabilistic analysis of cooled turbine blades, the variability of the internal geometry is of particular interest since this in combination with the external geometry variation leads to a change in wall thickness, which may influence stress and life significantly. Computed tomography (CT) represents a non-destructive technology that enables the detection of internal geometric features. The present paper address the uncertainty quantification (UQ) of CT for a high-pressure turbine (HPT) blade. Measurement data derived by optical techniques are used by way of comparison. A parametric turbine blade model is employed to quantify the deviations between optical and tomographic measurement data. Furthermore, five blades were cut open by wire erosion, which enables a comparative investigation in terms of wall thickness. The observed deviations are related to inherent sources of uncertainties in CT.

The present paper illustrates a robust way to extract relevant information of cooling holes from surface meshes of real turbine blades, which have been obtained by CT measurements. The X-ray based measurement technique results in low-quality meshes in certain regions of the component due to the very dense material of rotor blades. Thus the algorithms to extract cooling hole parameters must be robust for these low-quality meshes: The basic step of calculating the surface curvature information uses a regression based method that averages errors and provides a localized analytic surface description. Using geodesic distances as a node selection criterion for the surface function calculation, which is able to decouple curvature values from fluctuating node densities, is shown to improve the detection quality of the algorithm significantly. Based on characteristic curvature limits, clusters of cooling hole areas are distinguished. Finally, cylinders are fitted into the clustered mesh regions as a simple representation of the cooling holes. This procedure results in location and orientation parameters of the cylinders describing the general shape and location of the cooling holes of the investigated turbine blades.

Geometric variations caused by manufacturing scatter can influence the aerodynamic performance of turbomachinery components. In case of nozzle guide vanes (NGVs), the capacity is of particular importance due to its influence on the entire engine behaviour, since often the narrowest cross section of the turbine, which limits the capacity, is found in the first NGV stage. Within this scope, the present paper illustrates different methods in order to quantify the impact of geometric variations of high pressure turbine (HPT) NGVs with respect to capacity change during the development process. At first, in the design phase, a parametric CAD model of the NGV can be used to perform an initial assessment of the effect caused by different geometric variations onto capacity. The results of this study can for example be used to set the tolerances for the subsequent manufacturing process. As soon as the first real hardware components become available, their geometry can nowadays be accurately captured using optical measurement techniques. Consequently, reverse engineering (RE) methods can be used to enable numerical assessment of geometric variability since manufacturing scatter is determined and incorporated into the subsequent CFD analysis. The process to perform this assessment is described in the second part of the paper and its results are compared to the initial CAD-based study. The investigation is conducted using an example of a state-of-the-art NGV stage provided by Rolls-Royce.