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.

Due to the complexity of the internal flow field of compressors, the aerodynamic design and optimization of a highly loaded axial compressor with high performance still have three problems, which are rich engineering design experience, high dimensions, and time-consuming calculations. To overcome these three problems, this paper takes an engineering-designed 2.5-stage highly loaded axial flow compressor as an example to introduce the design process and the adopted design philosophies. Then, this paper verifies the numerical method of computational fluid dynamics. A new Bezier surface modeling method for the entire suction surface and pressure surface of blades is developed, and the multi-island genetic algorithm is directly used for further optimization. Only 32 optimization variables are used to optimize the rotors and stators of the compressor, which greatly overcome the problem of high dimensions, time-consuming calculations, and smooth blade surfaces. After optimization, compared with the original compressor, the peak efficiency is still improved by 0.12%, and the stall margin is increased by 2.69%. The increase in peak efficiency is mainly due to the rotors. Compared with the original compressor, for the second-stage rotor, the adiabatic efficiency is improved by about 0.4%, which is mainly due to the decreases of total pressure losses in the range of above 30% of the span height and 10%–30% of the chord length. Besides, for the original compressor, due to deterioration of the flow field near the tip region of the second-stage stator, the large low-speed region eventually evolves from corner separation into corner stall with three-dimensional space spiral backflow. For the optimized compressor, the main reason for the increased stall margin is that the flow field of the second-stage stator with a span height above 50% is improved, and the separation area and three-dimensional space spiral backflow are reduced.

This paper develops a novel design approach based on reinforcement learning, which can independently complete the mean line aerodynamic design process of the axial compressor. The approach combines Deep Deterministic Policy Gradient (DDPG) algorithm with mean line aerodynamic predicting program HARIKA to acquire the design experiences of the axial compressor. DDPG combines basic reinforcement learning algorithm with artificial neural networks to get continuous observation and give corresponding actions. After the specific modification of the DDPG, multi-objective optimization can be integrated into the design process. Under the guidance of this approach, the design and optimization processes of a 9-stage high-pressure axial compressor were completed without expert experiences. At the design point, the isentropic efficiency was 88.5% and the surge margin was 25%, which meets the requirement of the compressor’s efficiency and stability. And there was an increase of 13.4% and 22%, respectively, compared to the initial design. Moreover, through the analysis of the design results, the distributions of aerodynamic parameters conform to expert experiences. To verify the approach, traditional optimization methods, multi-island genetic optimization algorithm (GA), and multi-objective particle swarm optimization algorithm (MOPSO) were used to solve the same optimization problem. The DDPG optimized efficiency was 0.2% lower than the traditional optimization method, and the pressure ratio at the work point was, respectively, 0.6% and 2% higher than that of the MOPSO and GA, which proved the effectiveness of the new design approach. Furthermore, after training, this approach can give design results immediately near the specific design requirements, which is different from the traditional optimization methods. The new approach saved 93% evaluation steps compared to the GA in the −3% design point and finished the design process in 8 steps in the +3% design point, where GA failed to complete.

A new design optimization method is proposed for the problem of high-precision aerodynamic design of multistage axial compressors. The method mainly contains three aspects: full-blade surface parametrization can significantly reduce the number of control variables per blade row and increase the degrees of freedom of the leading edge blade angle compared with the traditional semiblade parametric method; secondly, the artificial bee colony algorithm improved initialization and food source exploration and exploitation mechanism to enhance the global optimization ability and convergence speed, and a distributed optimization system is built on the supercomputing platform based on this method; finally, a phased optimization strategy based on the “synchronous change in multirow blades” is proposed, and expert experience is introduced to achieve a better balance between exploration and exploitation. The optimization method is applied to the AL-31F four-stage low-pressure compressor. As a result, the adiabatic efficiency is improved by 0.67% and the surge margin is improved by 3.1% under the premise that the total pressure ratio and mass flow rate satisfy the constraints, which verifies the effectiveness and engineering practicality of the proposed optimization method in the field of multistage axial flow compressor aerodynamic optimization.

A novel parametric control method for the compressor blade, the full-blade surface parametric method, is proposed in this paper. Compared with the traditional parametric method, the method has good surface smoothness and construction convenience while maintaining low-dimensional characteristics, and compared with the semi-blade surface parametric method, the proposed method has a larger degree of geometric deformation freedom and can account for changes in both the suction surface and pressure surface. Compared with the semi-blade surface parametric method, the method only has four more control parameters for each blade, so it does not significantly increase the optimization time. The effectiveness of this novel parametric control method has been verified in the aerodynamic optimization field of compressors by an optimization case of Stage35 (a single-stage transonic axial compressor) under multi-operating conditions. The optimization case has brought the following results: the adiabatic efficiency of the optimized blade at design speed is 1.4% higher than that of the original one and the surge margin 2.9% higher, while at off-design speed, the adiabatic efficiency is improved by 0.6% and the surge margin by 1.3%.