ROBUST DESIGN OPTIMIZATION OF A COMPRESSOR ROTOR USING RECURSIVE COKRIGING BASED MULTI-FIDELITY UNCERTAINTY QUANTIFICATION AND MULTI-FIDELITY OPTIMIZATION

Abstract

Abstract This work focuses on the application of multi-fidelity methods for the robust design optimization of engine components. The robust design optimization approach yields geometric designs with high efficiency and low sensitivity to uncertainties from manufacturing and wear. The uncertainty quantification techniques required to evaluate the robustness are computationally expensive, which limits their use in robust optimization. Multi-fidelity methods offer a promising solution to reduce the computational cost while maintaining the accuracy in both uncertainty quantification and optimization. A Kriging and a multi-fidelity recursive Cokriging framework are developed and applied to a test function. Furthermore, a multi-fidelity super efficient global optimization algorithm is developed. The optimizer is surrogate based and handles constraints. The developed methods are then applied to a compressor test case of a high pressure compressor blade row with 9 uncertainty and 24 design parameters of the geometry. The 2.5 % quantile of the stage efficiency is used as a robustness measure and is optimized. Design bounds and performance constraints are applied. Various uncertainty quantification techniques are analyzed. A multi-fidelity uncertainty quantification approach is developed that combines simplified coarse-grid low-fidelity with high-fidelity results to reduce the computational cost while maintaining a high accuracy. Uncertainty quantification techniques of three fidelity levels are developed and used for the multi-fidelity approach in the design space. The robust design optimization is performed and the optimal designs obtained from the multi-fidelity approach show superior performance compared to existing robust design optima in the literature.