Manufacturing Uncertainties in High-Pressure Compressors

A method to deal with correlated manufacturing uncertainties based on the non-intrusive probabilistic collocation method and the principal component analysis is applied to a 1.5-stage high-pressure compressor. The uncertainties are defined based on a set of optical measurements, leading to realistic deformations. The parametric model, which is built based on the optical measurements and used to represent the blade geometry, allows the representation of the uncertainties by 15 correlated parameters. The results of the UQ computation are analysed in terms of computational cost and sensitivity of the quantities of interest with respect to the input uncertain parameters. It is shown that a level 1 sparse grid is sufficient to have a convergence of the two first statistical moments, which are the mean and the standard deviation and thus sufficient for the treatment of manufacturing uncertainties. Moreover, the sensitivity of the quantities of interest with respect to the input uncertainties are computed and compared with a Monte Carlo simulation found in the literature on the same test case. It is shown that the NIPColM coupled with the PCA allows reducing the computational cost by a factor 16 in comparison with the Monte Carlo simulation.