Robust Design of Compressor Blades Against Manufacturing Variations

The aim of this paper is to develop and illustrate an efficient methodology to design blades with robust aerodynamic performance in the presence of manufacturing uncertainties. A novel geometry parametrization technique is developed to represent manufacturing variations due to tolerancing. A Gaussian Stochastic Process Model is trained using DOE techniques in conjunction with a high fidelity CFD solver. Bayesian Monte Carlo Simulation is then employed to obtain the statistics of the performance at each design point. A multiobjective optimizer is used to search the design space for robust designs. The multiobjective formulation allows explicit trade-off between the mean and variance of the performance. A design, selected from the robust design set is compared with a deterministic optimal design. The results demonstrate an effective method to obtain compressor blade designs which have reduced sensitivity to manufacturing variations with significant savings in computational effort.