轴流压气机旋转失速建模与检测:基于确定学习理论与高阶Moore-Greitzer模型的研究

喘振和旋转失速是轴流压气机研究领域中重要而困难的问题. 本文基于确定学习理论及动态模式识别方法提出一个旋转失速初始扰动 近似准确建模和快速检测的方法. 首先，基于高阶Moore-Greitzer模型（Mansoux模型），利用确定学习理论提出一个对旋转失速初始扰动的内在系统动态的近似准确建模方法；其次，基于以上近似准确建模，利用 动态模式识别方法提出一个对旋转失速初始扰动的快速检测方法. 基于MIT的Mansoux-C2模型仿真研究验证了 所提方法的有效性. 最后，在北京航空航天大学航空发动机重点实验室的低速轴流压气机试验台上开展了试验研究. 通过对低速轴流压气机试验台参数进行测量，得到基于北航低速轴流压气机试验台的Mansoux模型. 通过对基于北航试验台Mansoux模型进行仿真研究，验证了所提方法的有效性.

Modeling and Detection of Rotating Stall in Axial Flow Compressors：Part I——Investigation on High-order M-G Models via Deterministic Learning

Rotating stall and surge are important and challenging problems in the area of axial compressors. This paper presents an approach for approximately accurate modeling and rapid detection of stall precursors based on deterministic learning. Firstly, based on the high-order compressor model (Mansoux model), a method for modeling the system dynamics corresponding to stall precursors is presented by employing deterministic learning algorithm; Secondly, a scheme for rapid detection of stall precursors is proposed by using dynamical pattern recognition algorithm; Thirdly, experiments are conducted on a low-speed research compressor of Beihang University. By measuring relevant parameters of the compressor, the Mansoux-Beihang model is obtained. Simulation studies on the Mansoux-C2 model and the Mansoux-Beihang model are included to show the effectiveness of the approach.