The complicated flow conditions and massive design parameters bring two main difficulties to the aerodynamic optimization of axial compressors: expensive evaluations and numerous optimization variables. To address these challenges, this paper establishes a novel fast aerodynamic optimization platform for axial compressors, consisting of a radial basic function (RBF)-based blade parameterization method, a data-driven differential evolution optimizer, and a computational fluid dynamic (CFD) solver. As a versatile interpolation method, RBF is used as the shape parameterization and deformation technique to reduce optimization variables. Aiming to acquire competitive solutions in limited steps, a data-driven evolution optimizer is developed, named the pre-screen surrogate model assistant differential evolution (pre-SADE) optimizer. Different from most surrogate model-assisted evolutionary algorithms, surrogate models in pre-SADE are used to screen the samples, rather than directly estimate them, in each generation to reduce expensive evaluations. The polynomial regression model, Kriging model, and RBF model are integrated in the surrogate model to improve the accuracy. To further save optimization time, the optimizer also integrates parallel task management programs. The aerodynamic optimization of a transonic rotor (NASA Rotor 37) is performed as the validation of the platform. A differential evolution (DE) optimizer and another surrogate model-assisted algorithm, committee-based active learning for surrogate model assisted particle swarm optimization (CAL-SAPSO), are introduced for the comparison runs. After optimization, the adiabatic efficiency, total pressure ratio, and surge margin are, respectively, increased by 1.47%, 1.0%, and 0.79% compared to the initial rotor. In the same limited steps, pre-SADE gets a 0.57% and 0.51% higher rotor adiabatic efficiency than DE and CAL-SAPSO, respectively. With the help of parallel techniques, pre-SADE and DE save half the optimization time compared to CAL-SAPSO. The results verify the effectiveness and the rapidity of the fast aerodynamic optimization platform.

This paper investigates the effect of rotor-stator spacing and operating conditions on compressor performance and the rotor-stator interaction mechanism. We focus on the interaction between the rotor wake and the stator’s flow field through several unsteady numerical simulations aiming at a transonic compressor stage in the equal radius flow path with two-axial spacing at three operating conditions. The simulation results indicate that the time-averaged efficiency almost declined by 0.6 points due to mixing loss in the spacing ranging from close to far at nominal and low load operating conditions. The deep upstream wake leads to corner separation at high load conditions due to close spacing, imposing a fluctuating efficiency at the frequency where the stator wake near the shroud oscillates, as found by the dominant frequency screening method. Under all conditions, the wake transport can reduce the static pressure on the pressure surface and weaken the hub leakage flow afterward.

This paper investigates the clocking effect in subsonic compressor element stages and the influence of design parameters on the flow mechanism. We focus on the relationship between the wake-induced separation loss and wake mixing loss and the unsteady mechanism in the wake flow process without considering the transition through several steady and unsteady numerical simulations aimed at a series of subsonic compressor element stages. The simulation results indicate that the performance difference at various indexing positions depends on the relationship between wake mixing loss and wake-induced separation loss for different compressor designs and operating conditions. Furthermore, the pressure transport caused by the negative jet of the Stator 0 wake in Rotor 1 creates a local acceleration region called SFAF, and a decrease in its absolute flow angle reduces the Stator 1 separation. Sufficient rim work of the rotor at highly loaded operating conditions is the basis for generating an effective SFAF. Furthermore, the fore-loading blade of Rotor 1 significantly reduces suction surface pressure drop, and a small angle between the stagger angles of Stator 0 and Rotor 1 increases the unsteady rotor load caused by the upstream wake to the total rotor load, both of which enhance SFAF.

An improved mean streamline loss model for splittered centrifugal/mixed-flow compressor impellers is presented. One-dimensional model predictions, CFD simulations and experimental results for Krain impeller are compared to confirm the model validity. A new description for the interaction between blade number and splitter blade length of a splittered mixed-flow compressor impeller with high hub/tip ratio is discussed and formulized. The optimal principal blade number and optimal splitter blade length corresponding to maximum impeller efficiency are selected by the new formulas studied. The results indicate that the improved model and new formulas have reasonable prediction effects for the impeller efficiency and the selection of blade number and splitter blade length. Blade height/pitch ratio can also be adopted as a selection reference especially for high hub/tip impellers. Effective blade number or equivalent solidity is only suitable for evaluating load capacity rather than efficiency in impeller design processes.

The refined aerodynamic design optimization of multistage compressors is a typical high-dimensional and expensive optimization problem. This study proposes an integrated surrogate model-assisted evolutionary algorithm combined with a Directly Manipulated Free-Form Deformation (DFFD)-based parametric dimensionality reduction method, establishing a high-precision and efficient global parallel aerodynamic optimization platform for multistage axial compressors. The DFFD method achieves a balance between flexibility and low-dimensional characteristics by directly controlling the surface points of blades, which demonstrates a particular suitability for the aerodynamic design optimization of multistage axial compressors. The integrated surrogate model enhances prediction accuracy by simultaneously identifying optimal solutions and the most uncertain solutions, effectively addressing highly nonlinear design space challenges. A three-stage axial compressor in a heavy-duty gas turbine is selected as the optimization object. The results demonstrate that the optimization task takes less than 48 h and achieves an improvement of 0.6% and 4% in the adiabatic efficiency and surge margin, respectively, while maintaining a nearly unchanged flow rate and pressure ratio at the design point. The proposed approach provides an efficient and reliable solution for complex aerodynamic optimization problems.