A Stochastic Approach for Performance Prediction of Aircraft Engine Components Under Manufacturing Uncertainty

Efficient modeling of uncertainty introduced by the manufacturing process is critical in the design of the components of the aircraft engines. In this study, a stochastic approach is presented to efficiently account for the geometric uncertainty, associated with the manufacturing process, in the accurate performance prediction of aircraft engine components. A semivariogram analysis procedure is proposed in this approach to quantify spatial variability of the uncertain geometric parameters based on the manufactured specimens. Karhunen-Loeve expansion is utilized to create a set of correlated random variables from the uncertainty data obtained by variogram analysis. The detailed model of the component is created accounting for the uncertainties quantified by these correlated random variables. A stochastic upscaling method is then utilized to form a simplified model that can represent this detailed model with high accuracy under uncertainties. Specifically, a parametric model generation process is developed to represent the detailed model using Bezier curves and the uncertainties are upscaled to the parameters of this parametric representation. The modal frequency-based reliability analysis of a turbine blade example is used to demonstrate the efficacy of the proposed approach. The application results show that the proposed method effectively captures the geometric uncertainties introduced by manufacturing while providing accurate predictions under uncertainties.