RESEARCH PROGRESS OF ADAPTIVE CONTROL METHODS FOR COMPRESSOR FLOW STABILITY
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摘要: 压气机流动稳定性自适应控制是未来智能航空发动机的一项关键技术. 基础研究需要回答3个关切: 如何描述系统的稳定性？如何改变系统的稳定性？如何监测系统的稳定性？为此, 本团队在压气机流动稳定性通用理论、壁面阻抗边界扩稳方法和在线实时失速预警技术等3个方面开展了系统深入的研究工作. (1)所发展的叶轮机流动稳定性通用理论既能包含流动非均匀性又能考虑叶片几何, 计算高效, 预测精度高, 为压气机气动/稳定性一体化设计提供了可靠的评估工具. (2)所发展的基于壁面阻抗边界调控策略的SPS (stall precursor-suppressed)机匣处理和泡沫金属机匣处理在扩稳、降噪和保持系统气动性能方面取得实质性进展, 采用等价分布源方法建立了包含机匣处理影响的压气机失速起始预测模型, 对SPS机匣处理和泡沫金属机匣处理关键结构参数进行敏感性分析, 使其具有明确的理论设计准则. 实验结果证实, SPS机匣处理通过抑制失速先兆波的非线性演化达到扩稳的目的, 在扩稳的同时可以保持压气机的压比和效率特性; 泡沫金属机匣处理可以实现扩稳和降噪的双重效果, 也具有良好的工程应用前景. (3)所发展的基于气动声学原理的实时失速预警方法将压气机失速预警时间提高到秒量级以上, 能够在线监测系统稳定性. 综合上述理论预测方法、扩稳技术和实时失速预警技术, 发展了闭环反馈自适应控制方法, 为未来智能航空发动机提供了一种自适应扩稳控制技术.Abstract: Adaptive control of compressor flow stability is a key technology of intelligent aeroengine in the future. Basic research needs to answer three concerns. How to describe the system stability? How to change the system stability? How to monitor the system stability? Therefore, our team has carried out systematic and in-depth research work in three aspects: general theory of compressor flow stability, stability margin enhancement method based on wall impedance boundary and real-time stall warning technology. (1) The developed general theory of flow instability in turbomachinery not only can consider the flow non-uniformity and blade geometry, but also has high calculation efficiency and great prediction accuracy, which provides a reliable evaluation tool for the integrated design of compressor aerodynamics and stability. (2) The developed SPS (stall precursor-suppressed) casing treatment and foam metal casing treatment based on wall impedance boundary control strategy have made substantial progress in enhancing stability margin and reducing noise while maintaining the aerodynamic performance of the compressor system. Equivalent distributed source method is employed to establish the stall inception prediction model considering the effect of casing, which is able to make sensitivity analysis on crucial structural parameters of SPS casing treatment and foam metal casing treatment so as to provide the clear theoretical design criterion. The experimental results show that SPS casing treatment achieves the purpose of stability enhancement by restraining the nonlinear evolution of stall precursor wave, while maintaining the pressure ratio and efficiency characteristics of the compressor; Foam metal casing treatment has favorable engineering application prospects for its double effects of improving stability and reducing noise. (3) The developed real-time stall warning approach based on aeroacoustic principle increases the stall warning time to more than seconds, and can monitor the system stability online. Combining the above theoretical prediction method, stability enhancement technology and real-time stall warning approach, the closed-loop feedback adaptive control method is developed, which provides an adaptive stability control technology for the future intelligent aeroengine.
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Key words:
- intelligent aeroengine /
- flow instability /
- casing treatment /
- foam metal /
- stall warning
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图 7 SPS机匣处理波涡相互作用示意图
Figure 7. Diagram of wave vortex interaction in SPS casing treatment
图 11 泡沫金属机匣处理在实验中的安装
Figure 11. Installation of foam metal casing treatment in experiment
图 18 可调几何参数的SPS机匣处理简图
Figure 18. Diagram of SPS casing treatment with adjustable geometric parameters
表 符号表
A, B, C, E, G, H, M, N, Q, R 系数矩阵
$ B $ 叶片数
$ {c_0} $ 声速
$ {c_v} $ 定容比热
$ DF $ 衰减因子
$ e $ 内能
$ F $ 与叶片力相关的系数矩阵
$ f $ 叶片力向量
$ {\boldsymbol{I}} $ 3阶单位阵
$ {\rm{i}} $ 虚数单位
$ k $ 压力波的波数
$ {k_{mn}} $ 特征方程的特征值
$ M $ 背景流马赫数
$ m $ 压力波的周向模态阶数
$ {m_{\text{c}}} $ 扰动量的周向波数
$ n $ 压力波的径向模态阶数
$ {n_{\text{r}}} $ 扰动量的径向波数
$ (n,\theta ,s) $ 流线坐标系
$ p $ 静压
$ q $ 任一气动物理量
$ \hat q $ 热源项
$ Rc $ 评估压力信号时间周期性的参数
$ R{c_{th}} $ $ Rc $的阈值
$ {R_g} $ 比气体常数
$ RS $ 相对速度
$ (r,\theta ,z) $ 圆柱坐标系
$ SPL $ 声压级, $ {\text{dB}} $
$ T $ 静温
$ T_{ij}^{'} $ Lighthill张量
$ t $ 时间
$ U $ 背景流绝对速度$ {\boldsymbol{u}} $ 绝对速度向量
$ u $ 径向绝对速度
$ v $ 周向绝对速度
$ W $ 相对速度
$ w $ 轴向绝对速度
$ x' = \left( {r',\theta ',z'} \right) $ 转子坐标系
$ {z_s} $ 传感器位置
$ \varGamma $ 环量
$ \gamma $ 比热比
$ \lambda $ 导热系数
$ \mu $ 动力黏性系数
$ {\boldsymbol{\varPi}} $ 二阶应力张量
$ \rho $ 密度
$ \tilde {\boldsymbol{\varPhi}} $ 扰动量幅值组成的列向量
$ {\phi _{mn}} $ 特征方程
$ \varphi $ 流量系数
$ \varOmega $ 转子转速, rad/s
$ \omega $ 复数特征频率
$ \nabla $ 梯度算子
$ \nabla \cdot $ 散度算子
下标:
$ {\text{i}} $ 特征频率的虚部
$ {\text{r}} $ 特征频率的实部
$ {\text{ref}} $ 基准声压
$ sound $ 声压
上标:
$ {\rm{T}} $ 矩阵转置
~ 扰动量幅值
$ ' $ 扰动量
$ - $ 背景物理量
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