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Stochastic axial compressor variable geometry schedule optimisation

机译：随机轴向压缩机可变几何进度优化。

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摘要

The design of axial compressors is dictated by the maximisation of flow efficiency at on design conditions whereas at part speed the requirement for operation stability prevails. Among other stability aids, compressor variable geometry is employed to rise the surge line for the provision of an adequate surge margin. The schedule of the variable vanes is in turn typically obtained from expensive and time consuming rig tests that go through a vast combination of possible settings. The present paper explores the suitability of stochastic approaches to derive the most flow efficient schedule of an axial compressor for a minimum variable user defined value of the surge margin. A genetic algorithm has been purposely developed and its satisfactory performance validated against four representative benchmark functions. The work carries on with the necessary thorough investigation of the impact of the different genetic operators employed on the ability of the algorithm to find the global extremities in an effective and efficient manner. This deems fundamental to guarantee that the algorithm is not trapped in local extremities. The algorithm is then coupled with a compressor performance prediction tool that evaluates each individual's performance through a user defined fitness function. The most flow efficient schedule that conforms to a prescribed surge margin can be obtained thereby fast and inexpensively. Results are produced for a modern eight stage high bypass ratio compressor and compared with experimental data available to the research. The study concludes with the analysis of the existent relationship between surge margin and flow efficiency for the particular compressor under scrutiny. The study concludes with the analysis of the existent relationship between surge margin and flow efficiency for the particular compressor under scrutiny.
机译：轴流式压缩机的设计取决于设计条件下的流量效率最大化，而在部分转速下，对运行稳定性的要求就很高。在其他稳定性辅助工具中，采用压缩机可变几何形状来升高喘振线，以提供足够的喘振裕度。反过来，可变叶片的时间表通常是从昂贵且费时的钻机测试中获得的，这些测试经过各种可能的设置。本文探讨了随机方法的适用性，以得出用户对喘振裕度的最小变量定义的轴流式压缩机的最大流量效率排程。专门开发了一种遗传算法，并针对四个代表性基准函数验证了其令人满意的性能。这项工作进行了必要的彻底调查，以调查所采用的不同遗传算子对算法以有效和高效的方式找到全球极端的能力的影响。这被认为是保证该算法不会陷入局部肢体的根本。然后，该算法与压缩机性能预测工具耦合，该工具通过用户定义的适应度函数评估每个人的性能。由此可以快速而廉价地获得符合规定的喘振裕度的最有效流量的时间表。产生了现代八级高旁路比压缩机的结果，并与可用于研究的实验数据进行了比较。该研究的结论是对经过仔细审查的特定压缩机的喘振裕度和流量效率之间存在的关系进行了分析。该研究的结论是对经过仔细审查的特定压缩机的喘振裕度和流量效率之间存在的关系进行了分析。

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  • 来源
    《Aerospace science and technology》 |2011年第5期|p.366-374|共9页
  • 作者

    Luis Gallar; Manuel Arias; Vassilios Pachidis; Riti Singh;

  • 作者单位

    Department of Power and Propulsion, Cranfield University, Gas Turbine Engineering Croup, Cranfield, Bedfordshire, MK430AL, England, United Kingdom;

    Department of Power and Propulsion, Cranfield University, Gas Turbine Engineering Croup, Cranfield, Bedfordshire, MK430AL, England, United Kingdom;

    Department of Power and Propulsion, Cranfield University, Gas Turbine Engineering Croup, Cranfield, Bedfordshire, MK430AL, England, United Kingdom;

    Department of Power and Propulsion, Cranfield University, Gas Turbine Engineering Croup, Cranfield, Bedfordshire, MK430AL, England, United Kingdom;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    axial compressor; variable geometry; genetic algorithm; optimisation; compressor performance;

    机译：轴流压气机;可变几何形状;遗传算法;优化;压气机性能;

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