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.

Parameterized optimization methods are effective approaches for achieving high aerodynamic performance in compressors. Traditional parameterized optimization methods rely on a designer's preselected control parameter layout (including control frame orientation, point density distribution, control point displacement direction, number of variables, and variable ranges), which are purely based on empirical knowledge without sufficient theoretical basis. This paper selects the free-form deformation (FFD) method and Bayesian algorithm as the parameterization method and optimization algorithm for compressor airfoil optimization and studies the influence of FFD control parameter layouts on aerodynamic optimization performance. Additionally, an adaptive optimization method for control parameters based on FFD is proposed, where the orientation and density of the control framework can be incorporated as variables into the control parameters. During the optimization process, the range of design variables is adaptively expanded. A comparison between FFD optimization results based on B-spline and Bernstein basis functions shows that the former achieves an average performance improvement of 4% relative to the latter. Furthermore, an optimization method with an infinitely expandable boundary based on Bernstein basis FFD is proposed, which improves the performance by 12% compared to general adaptive boundary expansion methods.

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.