Redesign of casing treatment for a transonic centrifugal compressor based on a hybrid global optimization method

Abstract

As a typical black-box problem, recirculating casing treatment (RCT) optimization of compressor stages is computationally intensive and time consuming, even though surrogate models are usually employed. In order to improve efficiency and robustness of the optimization, an expected-improvement (EI) based hybrid global optimization (EHGO) algorithm is developed by coupling an EI-based surrogate model with a hybrid optimization algorithm. Highly nonlinear and multiple modality mathematical tests show that the EHGO algorithm is able to create a high-fidelity surrogate model near targeted regions with less evaluated samples, and to obtain the global optimal solution simultaneously. The RCT of a compressor stage is optimized based on this algorithm. The number of CFD simulations required for obtaining an optimum solution is greatly reduced, as compared to similar studies using conventional methods. The optimization results show that the aerodynamic performance is improved over the whole speed line and the flow range is also extended. The dominant factors for the performance improvements and the enhanced stall margin are addressed by analyzing the local flow characteristics before and after optimization. It is found that those factors include: removing a larger amount of low-momentum fluid, achieving a more uniform flow of impeller passage in circumferential direction, and reducing the radial distortion of impeller inlet flow. The proposed algorithm has the potential to considerably speed up the optimization process and make the optimization much more accessible. It can be generalized to deal with other computationally intensive black-box problems, for example, turbomachinery optimization.