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基于骨架特性的压气机可调叶片模型特性修正

李斌 严红明 李方刚 曹传军 杜辉

李斌, 严红明, 李方刚, 等. 基于骨架特性的压气机可调叶片模型特性修正[J]. 航空动力学报, 2024, 39(7):20220459 doi: 10.13224/j.cnki.jasp.20220459
引用本文: 李斌, 严红明, 李方刚, 等. 基于骨架特性的压气机可调叶片模型特性修正[J]. 航空动力学报, 2024, 39(7):20220459 doi: 10.13224/j.cnki.jasp.20220459
LI Bin, YAN Hongming, LI Fanggang, et al. Compressor variable stator vane model performance correction based on backbone map[J]. Journal of Aerospace Power, 2024, 39(7):20220459 doi: 10.13224/j.cnki.jasp.20220459
Citation: LI Bin, YAN Hongming, LI Fanggang, et al. Compressor variable stator vane model performance correction based on backbone map[J]. Journal of Aerospace Power, 2024, 39(7):20220459 doi: 10.13224/j.cnki.jasp.20220459

基于骨架特性的压气机可调叶片模型特性修正

doi: 10.13224/j.cnki.jasp.20220459
基金项目: 国家科技重大专项(2017-Ⅱ-0001-0013)
详细信息
    作者简介:

    李斌(1990-),男,高级工程师,硕士,从事航空发动机总体性能模型开发相关工作。E-mail:libin_1860@126.com

  • 中图分类号: V231.3

Compressor variable stator vane model performance correction based on backbone map

  • 摘要:

    基于骨架特性原理,建立压气机可调叶片(VSV)变几何性能模型。介绍了压气机骨架特性处理方法的优点和适用性。基于压气机骨架特性处理方法,开发一种VSV模型修正方法,通过调整流量系数、功系数和损失系数的比例修正系数,实现压气机特性随VSV任意角度变化的高精度建模。建立自动优化方法,提高执行效率,减少人工干预。同时,与某型压气机VSV联调试验结果进行对比,相对误差达到小于0.2%的水平,验证了修正方法的正确性和精度;选用比例系数修正特定骨架特性的修正方法,可推广至压气机特性的其他二次影响修正(如雷诺数效应)。

     

  • 图 1  传统的压气机特性

    Figure 1.  Traditional compressor map

    图 2  压气机三种工作状态的效率变化示意图

    Figure 2.  Compressor efficiency map in three operation modes

    图 3  功系数-流量系数曲线

    Figure 3.  Work coefficient as a function of flow coefficient

    图 4  损失系数-功系数曲线

    Figure 4.  Loss coefficient as a function of work coefficient

    图 5  最小损失点骨架特性曲线

    Figure 5.  Min-loss points backbone characteristics

    图 6  损失系数偏移量随$\Delta \varPsi\cdot |\Delta \varPsi |$变化曲线

    Figure 6.  Loss coefficient offset as a function of $\Delta \varPsi \cdot |\Delta \varPsi |$

    图 7  虚拟马赫数随$ \Delta \varPsi $变化曲线

    Figure 7.  Virtual Mav as a function of $ \Delta \varPsi $

    图 8  骨架特性转换结果对比

    Figure 8.  Backbone map compared with test result

    图 9  VSV调节效果

    Figure 9.  Influence of change of VSV

    图 10  自动优化算法流程图

    Figure 10.  Flow chart of automatic optimization

    图 11  流量系数的修正系数

    Figure 11.  Flow coefficient scalar as a function of $ {\rm{d}}\varphi $

    图 12  功系数的修正系数

    Figure 12.  Work coefficient scalar as a function of $ {\rm{d}}\varphi $

    图 13  损失系数的修正系数

    Figure 13.  Loss coefficient scalar as a function of $ {\rm{d}}\varphi $

    图 14  0.98Nc 转速修正模型与试验特性对比

    Figure 14.  Corrected model compared with test data at 0.98Nc

    图 15  0.75Nc 转速修正模型与试验特性对比

    Figure 15.  Corrected model compared with test data at 0.75Nc

    表  1  0.98Nc转速修正模型的相对误差

    Table  1.   Relative error of corrected model at 0.98Nc

    $ {\rm{d}}\varphi $/(°)$ {\sigma _1} $/%$ {\sigma _2} $/%$ {\sigma _3} $/%
    −3.00.0930.0180.186
    −1.00.1760.0680.144
    1.00.1220.0260.145
    3.00.0750.0060.107
    下载: 导出CSV

    表  2  0.75Nc转速修正模型的相对误差

    Table  2.   Relative error of corrected model at 0.75Nc

    ${\rm{d}}\varphi $/(°)$ {\sigma _1} $/%$ {\sigma _2} $/%$ {\sigma _3} $/%
    −3.00.4300.1850.426
    −1.00.3950.2700.289
    1.00.2750.1230.394
    3.00.2920.0900.367
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-26
  • 网络出版日期:  2023-09-22

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