Abstract
The multi-objective optimization of compressor cascade rotor blade is important for aero engine design. Many conventional approaches are thus proposed; however, they lack a methodology for utilizing existing design data/experiences to guide actual design. Therefore, the conventional methods require and consume large computational resources due to their need for large numbers of stochastic cases for determining optimization direction in the design space of problem. This paper proposed a Reinforcement Learning method as a new approach for compressor blade multi-objective optimization. By using Deep Deterministic Policy Gradient (DDPG), the approach modifies the blade profile as an intelligent designer according to the design policy: it learns the design experience of cascade blade as accumulated knowledge from interaction with the computation-based environment; the design policy can thus be updated. The accumulated computational data is therefore transformed into design experience and policies, which are directly applied to the cascade optimization, and the good-performance profiles can be thus approached. In a case study provided in this paper, the proposed approach is applied on a blade profile, which is thus optimized in terms of total pressure loss and laminar flow area. Compared with the initial profile, the total pressure loss coefficient is reduced by 3.59%, and the relative laminar flow area at the suction surface is improved by 25.4%.
Highlights
For complex aerodynamic design cases, such as turbomachinery blades, single optimization objective is not enough, according to the complicated flow phenomena
A multi-objective optimization method based on reinforcement learning technique is proposed and applied in a hybrid optimization of the compressor cascade blade profile on the total pressure loss and the laminar flow area
An ANN-based surrogate model is used as the environment in the Deep Deterministic Policy Gradient (DDPG) network, feeding back the aerodynamic parameters corresponding to the deformed profiles with surrogate model trained with parameterized profiles and aerodynamic data calculated via CFD solver
Summary
For complex aerodynamic design cases, such as turbomachinery blades, single optimization objective is not enough, according to the complicated flow phenomena. With the development of computing power, stochastic methods are more and more widely used in aerodynamic shape optimization, e.g., Particle Swarm Optimization (PSO) [12], Simulated Annealing (SA) [13], Genetic Algorithm (GA) [14], and Evolutionary Algorithm (EA) [15], which are good at finding global optima, in many multi-objective optimizations [16,17,18,19]. Stochastic methods need large numbers of stochastic cases to decide evolutionary trends via probability-based selection, whereas the existing data is not capable of being utilized in the above process to determine optimization direction It results in the drawbacks of stochastic methods, like its slow convergence and high computational cost (especially for high-fidelity computational fluid dynamics (CFD)). The DDPG network can learn the design experience of cascade blade with the feedback according to the aerodynamic parameters, which can guide the modification of the profile optimization.
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