Conditional sampling and experiment design for quantifying manufacturing error of transonic airfoil

We use conditional sampling of Gaussian stochastic processes to simulate the effect of manufacturing error on an RAE 2822 airfoil in transonic flow. Through a singular value decomposition of randomly sampled input-output Jacobian matrices, we show that the effect of the input high dimensional geometric uncertainty on the output aerodynamic performance can be largely attributed to a few dominant modes of the geometric perturbation. Based on this dimensionality reduction approach, we derive the ”measurement information functions” of the dominant modes. We demonstrate that the measurement information function can be used to select a small number of inspection points on the airfoil surface, so that the uncertainty in the aerodynamic performance can be significantly reduced by measuring the manufacturing error on these points.

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