Reducing costs and development times are two of the main challenges for aircraft engines manufacturers. Analysis shows that the main troubles encountered during the industrialization phase are due to choices made during the first steps, such as the preliminary design of the compressor throughflow (flowpath and velocity triangles). Therefore, constraints and needs from the later phases have to be taken into account as early as possible. A deterministic optimization method for automated compressor throughflow design has been developed to achieve these objectives, improving efficiency and surge margin while modifying the design parameters. Nevertheless, variability between the theoretical geometry and the actual one may occur because of the manufacturing process or the damages encountered during the engine life cycle. Depending on their magnitude, these differences can affect the engine performance. To consider these random phenomena from the design step, the deterministic optimization is coupled with a probabilistic approach, based on a robust design methodology which aims at guarantee the engine performance despite geometrical variability. This article deals with geometrical robustness. It presents a robust design methodology and introduces a capability function used to optimize the outputs of a compressor model while minimizing their standard deviation. The model has two kinds of inputs: the design factors, which are known by both designer and manufacturer, and the noise factors, that are just known by their mean value and their standard deviation. As robust design requires a large number of calculations, it is interesting to work with an approximated physical model such as a response surface, generated through the computation of a suitable design of experiments. This method has been successfully applied to the design of a Snecma Moteurs high-pressure compressor.

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