The mean-line method, as the corner stone of preliminary aerodynamic design of axial-flow compressors, is heavily dependent on the accuracy and robustness of empirical prediction models, mainly the deviation models and loss models. A large number of such models have been developed, however, a comprehensive evaluation of their prediction capabilities was lacked yet. To carry out the accuracy and sensitivity analysis of these prediction models developed in the both academic and industry communities, we here developed a one-dimensional mean-line method which implements several widely used deviation and loss models. Then, the developed mean-line method was applied to predict the speed-lines of aerodynamic performance for three representative transonic axial-flow compressors, i.e., NASA Rotor 35, NASA Stage 35 and NASA 74A first front three-stage. The parallel coordinates method was particularly adopted to effectively perform the sensitivity analysis of totally 2448 combinations of deviation and loss models through a heuristic comparison of the model predictions with the available experimental data. The accuracy analysis indicates that, by using the best model combinations, the prediction error of peak efficiency point is generally kept below 2% whereas that of surge margin varies significantly from 3.03% to 18.93%. However, the most accurate model combination is dependent on the compressor type and rotational speed. The sensitivity analysis shows that the prediction robustness is remarkably influenced by the deviation model accounting for axial velocity ratio effect, the design shock loss model and the off-design total loss model. This work provides the design engineers with prediction model selection, and the model developers with prediction improvement direction for axial-flow compressors.

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With great development in numerical optimization and wide application of computational fluid dynamic methods, the three-dimensional design optimization of multi-stage axial flow compressor will become a routine practice in the future. The common critical issue arisen from the practice is how to carry out the three-dimensional design optimization of multi-stage axial flow compressor at an affordable design cost. To tackle this problem, a three-dimensional design optimization method is developed, in which reference vector guided evolutionary algorithm, the ensemble of surrogate models, adaptive sampling method and computational fluid dynamics (CFD) simulation are integrated, to improve the adiabatic efficiency and broaden the stability margin. The ensemble of surrogate model consists of linear regression model, support vector regression model, Kriging model and radial basis function. The blade lean and twist are set as design variables for all the stators while for the rotors only the blade twist is considered. By taking advantage of these advanced methods, a 6.5-stage axial flow compressor with totally 60 design variables is optimized at design speed-line. CFD calculations show that the adiabatic efficiency at design point is increased by 0.88%. The stall margin is increased from 12.72% to 14.08%, and the choke margin is increased from 20.41% to 21.35%. Flow analysis of the optimal compressor indicates that the blade lean is mainly responsible for suppressing the endwall flow separation while the blade twist for the stage match between adjacent blade rows. This work is of great significance for three-dimensional multi-objective design optimization of multi-stage axial flow compressor.

Higher efficiency and wider stability of performance map for advanced axial flow compressor will be potentially achieved by using data mining to gain a deep insight into complex correlations between aerodynamic performance and three-dimensional geometry parameters with the rapid growth of computational resources and artificial intelligence. However, few research works have been found on using the data mining that is independent of design optimization to extract priori design guidelines for multi-stage axial flow compressor mainly due to the lack of proper data mining method focused on interpretation of metamodel with full use of limited time-consuming computational fluid dynamics dataset. To tackle this issue, a metamodel-interpreted data mining framework is developed in which extreme gradient boosting metamodel combined with Shapley additive explanation model are employed to locally interpret the feature importance of each sample in the computational fluid dynamics dataset and extract the design guidelines in terms of the most influential geometry parameters and their beneficial variation directions. The developed method is applied to data mining of design guidelines for efficiency and stability enhancement of a front 3.5-stage transonic axial-flow compressor. The results show that followed by the design guidelines, the stall margin at part speed is widened to 5.87% with adjustment of blade lean and twist and further to 23.31% with additional adjustment of variable stators. The peak adiabatic efficiency at design speed is improved by 0.06% in spite of extremely limited potential for efficiency enhancement of the original design.