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压气机流动稳定性自适应控制方法研究进展

许登科 董旭 徐瑞泽 李佳 孙大坤 孙晓峰

许登科, 董旭, 徐瑞泽, 李佳, 孙大坤, 孙晓峰. 压气机流动稳定性自适应控制方法研究进展. 力学学报, 2022, 54(3): 559-576 doi: 10.6052/0459-1879-21-560
引用本文: 许登科, 董旭, 徐瑞泽, 李佳, 孙大坤, 孙晓峰. 压气机流动稳定性自适应控制方法研究进展. 力学学报, 2022, 54(3): 559-576 doi: 10.6052/0459-1879-21-560
Xu Dengke, Dong Xu, Xu Ruize, Li Jia, Sun Dakun, Sun Xiaofeng. Research progress of adaptive control methods for compressor flow stability. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(3): 559-576 doi: 10.6052/0459-1879-21-560
Citation: Xu Dengke, Dong Xu, Xu Ruize, Li Jia, Sun Dakun, Sun Xiaofeng. Research progress of adaptive control methods for compressor flow stability. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(3): 559-576 doi: 10.6052/0459-1879-21-560

压气机流动稳定性自适应控制方法研究进展

doi: 10.6052/0459-1879-21-560
基金项目: 国家自然科学基金(51822601, 51790514, 51906004), 国家科技重大专项(2017-II-0005-0018)和北京航空航天大学博士研究生卓越学术基金资助项目
详细信息
    作者简介:

    孙大坤, 教授, 主要研究方向: 航空压气机流动稳定性. E-mail: sundk@buaa.edu.cn

  • 中图分类号: V231.3

RESEARCH PROGRESS OF ADAPTIVE CONTROL METHODS FOR COMPRESSOR FLOW STABILITY

  • 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.

     

  • 图  1  流线坐标系

    Figure  1.  Streamline coordinate system

    图  2  NASA Stage35子午面模型预测结果[38]

    Figure  2.  Prediction result of NASA Stage35 via meridian surface model[38]

    图  3  NASA低速离心压气机径向展开模型预测结果[41]

    Figure  3.  Prediction result of NASA low-speed centrifugal compressor via radial expansion model[41]

    图  4  NASA Rotor37流线模型预测结果[40]

    Figure  4.  Prediction result of NASA Rotor37 via streamline model[40]

    图  5  叶片后掠对NASA Rotor37流动稳定性的影响[39]

    Figure  5.  Effect of backward swept blades on flow stability of NASA Rotor37[39]

    图  6  不同机匣处理下的转子最大效率与失速裕度提升[42]

    Figure  6.  Relation between rotor maximum efficiency and stall margin improvement[42]

    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  8  不同结构参数SPS机匣处理扩稳效果理论预测[48]

    Figure  8.  Stability prediction of SPS casing treatment with different structural parameters[48]

    图  9  SPS机匣处理在亚声速压气机TA36上的扩稳实验结果[31]

    Figure  9.  Experimental results of SPS casing treatment on subsonic compressor TA36[31]

    10  SPS机匣处理在跨声速压气机J69上的扩稳实验结果[49]

    10.  Experimental results of SPS casing treatment on transonic compressor J69[49]

    图  10  SPS机匣处理在跨声速压气机J69上的扩稳实验结果[49](续)

    Figure  10.  Experimental results of SPS casing treatment on transonic compressor J69[49] (continued)

    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  12  带L1 CT的压气机特性线[52]

    Figure  12.  Compressor characteristics with L1 CT[52]

    图  13  带L2 CT的压气机特性线[52]

    Figure  13.  Compressor characteristics with L2 CT[52]

    图  14  设计工况下L1 CT50, L2 CT50和光壁机匣下压气机的声压级[52]

    Figure  14.  Sound pressure levels of compressors with L1 CT50, L2 CT50 and solid wall casing at design operating condition[52]

    图  15  压力传感器安装位置示意图

    Figure  15.  Installation position of pressure sensor

    图  16  失速先兆实验测试结果[54]

    Figure  16.  Experimental results of stall precursor[54]

    图  17  设计转速下在线预警结果[54]

    Figure  17.  On-line stall warning at design rotating speed[54]

    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

    图  19  扩稳目标5.5%下实现的全转速开环稳定性控制效果[56]

    Figure  19.  Open-loop stability control at 5.5% object of stall margin enhancement[56]

    图  20  自适应扩稳控制流程图

    Figure  20.  Flowchart of adaptive control

    图  21  在线自适应控制监测结果[56]

    Figure  21.  Online adaptive control results[56]

    图  22  全转速SPS机匣处理自适应控制效果[56]

    Figure  22.  Adaptive control effect of SPS casing treatment at different rotating speeds[56]

    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
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-31
  • 录用日期:  2022-01-13
  • 网络出版日期:  2022-01-14
  • 刊出日期:  2022-03-18

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