级环境下跨声速对转压气机优化设计研究

Optimization of Transonic Counter-Rotating Compressor in Stage-Environment

关键词:  跨声速  对转压气机  级环境  优化设计

Key words:  Transonic  Counter-rotating compressor  Stage environment  Optimization design

摘要:

为研究不同优化策略对跨声速对转压气机性能以及内部流动机理的影响规律，运用人工神经网络结合遗传算法在级环境下对下游转子轮毂型线和积叠规律分别进行了三维优化设计。结果表明:轮毂型线优化和积叠规律优化分别使下游转子效率提升了1.19%和2.87%，整机效率分别提升了0.48% 和0.63%。轮毂型线优化改善了下游转子叶根区域靠近压力面的分离流动，降低了流动损失；下游转子积叠规律优化有效抑制了下游转子叶根角区分离流动，同时降低了叶顶泄漏流损失；轮毂型线优化对根部角区的调控能力优于积叠规律优化，二者的组合优化有望进一步提升对转压气机性能。

Abstract:

To study the influences of optimization strategies on the internal flow and performance of contra-rotating compressor (TCRC)，this paper employed the artificial neural networks combined with the genetic algorithm to three-dimensional redesign the hub geometric profile and stacking law of the downstream rotor. Results revealed that the efficiency of downstream rotor was promoted by 1.19% and 2.87% and the whole compressor efficiency was promoted by 0.48% and 0.63% through optimizing the hub geometric profile and stacking law，respectively. The optimization of the hub profile could effectively improve the large-scale separation on the pressure side of the blade root of downstream rotor. While the optimization of blade stacking law could suppress the flow separation near the corner area of blade root and additionally it could reduce the loss tip leakage flow. The control of hub profile optimization shows a more effective manipulation on the blade root flow than the stacking law optimization. The combination of these two optimization strategies is expected to achieve further improvement for the performance of the contra-rotating compressor.