COMPRESSOR VARIABLE GEOMETRY SCHEDULE OPTIMISATION USING GENETIC ALGORITHMS

被引:0
|
作者
Gallar, L. [1 ]
Arias, M. [1 ]
Pachidis, V. [1 ]
Pilidis, P. [1 ]
机构
[1] Cranfield Univ, Sch Engn, Dept Power & Prop, Cranfield MK43 0AL, Beds, England
关键词
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Variable geometry blade rows in axial compressors are devised to fulfil different requirements. Main objectives include their role as a "part speed crutch" to push the front stages out of surge at low spool speeds, modulation of the power output in industrial machines given the fact that the spool needs to run at synchronous speed with the electric generator frequency and they can also be re-staggered to attain a modified capacity (usually upflowed) of the same baseline compressor The operating schedule of the variable vanes is typically obtained from expensive and time consuming performance rig tests in which a large number of possible combinations are compared. In principle, the final choice is dictated by the pursuit of high efficiency at high rotational speeds and increased surge margin at low speeds where large excursions away from the design point are expected. The aim of this work is to integrate a validated genetic algorithm optimiser within an industry proprietary mean line compressor performance prediction code to maximise the machine efficiency while keeping an adequate user-defined value of the surge margin. In so doing, an optimised variable geometry schedule is derived, together with a modified range of rotational speeds for each given operating point Nevertheless, aware of the detrimental consequences to the whole engine performance that the new arrangement can cause, the whole engine response for the new settings has been investigated In this regard and to a first order, the working line on the compressor map is considered unaffected by the setting of the variable vanes and the effect of the spool speed variation on the turbine operation is accounted for by a reduction in turbine efficiency proportional to any fall in the shaft speed. Results for a state of the art eight stage compressor show a marked improvement for the coupled compressor-turbine efficiency particularly at low spool speeds for a sensible value of the surge margin. Free from the surge margin constraint the efficiency is further Increased at the expense of a hindered compressor operational stability The work is intended to continue with the incorporation of bleeds and power off take in the calculations for the sake of a greater applicability of the tool.
引用
收藏
页码:425 / 434
页数:10
相关论文
共 50 条
  • [1] Stochastic axial compressor variable geometry schedule optimisation
    Gallar, Luis
    Arias, Manuel
    Pachidis, Vassilios
    Singh, Riti
    AEROSPACE SCIENCE AND TECHNOLOGY, 2011, 15 (05) : 366 - 374
  • [2] Zeolite structure determination using genetic algorithms and geometry optimisation
    Liu, Xuehua
    Valero, Soledad
    Argente, Estefania
    Sastre, German
    FARADAY DISCUSSIONS, 2018, 211 : 103 - 115
  • [3] Compressor Schedule Optimization for a Refrigerated Warehouse Using Metaheuristic Algorithms
    Biedrzycki, Rafal
    Kwiatkowski, Kamil
    Cichosz, Pawel
    2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), 2021, : 201 - 208
  • [4] Automated construction schedule optimisation using genetic algorithm
    Srimathi K.R.
    Padmarekha A.
    Anandh K.S.
    Asian Journal of Civil Engineering, 2023, 24 (8) : 3521 - 3528
  • [5] Bus route and schedule optimisation using a genetic algorithm
    Dhonghaile, S. M. Ni
    O'Brien, E. J.
    Proceedings of The Seventh International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, 2003, : 81 - 82
  • [6] Overall Schedule OPTIMIZATION USING GENETIC ALGORITHMS
    Amin, Mahmoud
    Ghaly, Athnasious
    Ayad, Fredy
    Hosny, Ossama
    PROCEEDINGS OF THE CANADIAN SOCIETY OF CIVIL ENGINEERING ANNUAL CONFERENCE 2022, VOL 3, CSCE 2022, 2024, 359 : 449 - 461
  • [7] Overall Schedule OPTIMIZATION USING GENETIC ALGORITHMS
    Amin, Mahmoud
    Ghaly, Athnasious
    Ayad, Fredy
    Hosny, Ossama
    PROCEEDINGS OF THE CANADIAN SOCIETY OF CIVIL ENGINEERING ANNUAL CONFERENCE 2022, VOL 4, CSCE 2022, 2024, 367 : 449 - 461
  • [8] Optimisation Geometry and Its Implications for Optimisation Algorithms
    Pauley, Michael
    Manton, Jonathan H.
    2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP), 2017,
  • [9] Sequential process optimisation using Genetic Algorithms
    Oduguwa, V
    Tiwari, A
    Roy, R
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII, 2004, 3242 : 782 - 791
  • [10] Packet transmission optimisation using Genetic Algorithms
    Withall, M
    Hinde, C
    Stone, R
    Cooper, J
    DEVELOPMENTS IN APPLIED ARTIFICIAL INTELLIGENCE, 2003, 2718 : 653 - 661