Aerodynamic robustness optimization and design exploration of centrifugal compressor impeller under uncertainties

• Published in: International journal of heat and mass transfer 2021-12, Vol.180, p.121799, Article 121799
• Main Authors: Tang, Xinzi, Gu, Nengwei, Wang, Wenbin, Wang, Zhe, Peng, Ruitao
• Format: Article
• Language: English
• Subjects: Aerodynamic stability; Centrifugal compressor; Centrifugal compressors; Design; Design optimization; Design parameters; Impellers; Model accuracy; Multiple objective analysis; Neural networks; Optimization; Pressure ratio; Robustness; Scientific visualization; Self-organizing map; Surrogate based optimization; Turbomachinery; Uncertainty
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2021.121799

•An efficient approach for aerodynamic robustness optimization.•A neural-network-based Kriging model for performance modelling.•Uncertainty quantification coupled in optimization.•Benchmarking study for centrifugal compressor impeller.•Design correlation between design inputs and performance parameters. Aerodynamic robustness optimization of centrifugal compressor impeller under multiple uncertainties is an arduous task, due to high dimension, meta modelling workload and trial and error optimization, and an automatic optimization process is often treated as a black box, the underlying mechanism are often not well understood and explored. This paper aims to provide an efficient robustness optimization procedure and explore the underlying physical mechanism for centrifugal compressor impeller under operational and geometric uncertainties. A novel approach is proposed for the multi-objective aerodynamic robustness optimization and design exploration of a centrifugal compressor impeller. A neural-network-based Kriging model is constructed and integrated into the aerodynamic robustness optimization The correlation between the design variables and performance parameters are explored from optimization and directly visualized by self-organizing mapping. Compared to the initial impeller, the average pressure ratio of the optimized impeller increases by 9.3%, the average isentropic efficiency increases by 6.7%, the standard deviations of pressure ratio and efficiency decrease by 7.5% and 15.4% respectively, and the acoustic power level decreases by 11dB. The neural-network-based Kriging model exhibits preferable accuracy for uncertain approximation modeling. The data visualization and interpretation facilitate designers to perform efficient design optimizations. The proposed approach supports design explorations for different applications of turbomachinery.