IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v106y2016icp590-601.html
   My bibliography  Save this article

Optimization of cantilevered stators in an industrial multistage compressor to improve efficiency

Author

Listed:
  • Lu, Hanan
  • Li, Qiushi
  • Pan, Tianyu

Abstract

This paper presents an optimization of transonic cantilevered stators based on the combination of sweep and lean in a 5-stage axial compressor to improve its adiabatic efficiency and maintain its total pressure ratio by employing a time-saving integrated optimization design system. The system combines design of experiment with artificial neural network and computational fluid dynamics. The optimization is focused on the first stator stage and the motivation is to understand the mechanism of the performance improvement. It shows that the effect of combinational sweep and lean not only reduces the size of the transonic regions on the blade suction surface near the leading edge but also decreases the leakage loss, thus resulting in lower aerodynamic losses in hub region. Simultaneously, the downstream rotor achieves a higher aerodynamic performance due to a better matching for the improvement of the inlet working conditions. After optimization, the stator loss is reduced by 3.7%, whereas the adiabatic efficiency of the downstream rotor is increased by 0.64%, thus achieving an increment of 0.22% of peak efficiency of the entire compressor. The results show that cantilevered stators with the characteristic of sweep and lean have a significant potential to improve the performance of multistage axial-flow compressor.

Suggested Citation

  • Lu, Hanan & Li, Qiushi & Pan, Tianyu, 2016. "Optimization of cantilevered stators in an industrial multistage compressor to improve efficiency," Energy, Elsevier, vol. 106(C), pages 590-601.
  • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:590-601
    DOI: 10.1016/j.energy.2016.03.109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216303565
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2016.03.109?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Baklacioglu, Tolga & Turan, Onder & Aydin, Hakan, 2015. "Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks," Energy, Elsevier, vol. 86(C), pages 709-721.
    2. Wang, Xinli & Cai, Wenjian & Lu, Jiangang & Sun, Youxian & Zhao, Lei, 2015. "Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm," Energy, Elsevier, vol. 82(C), pages 939-948.
    3. Park, Jungsoo & Lee, Kyo Seung & Kim, Min Su & Jung, Dohoy, 2014. "Numerical analysis of a dual-fueled CI (compression ignition) engine using Latin hypercube sampling and multi-objective Pareto optimization," Energy, Elsevier, vol. 70(C), pages 278-287.
    4. Ganesan, P. & Rajakarunakaran, S. & Thirugnanasambandam, M. & Devaraj, D., 2015. "Artificial neural network model to predict the diesel electric generator performance and exhaust emissions," Energy, Elsevier, vol. 83(C), pages 115-124.
    5. Benini, Ernesto & Biollo, Roberto, 2007. "Aerodynamics of swept and leaned transonic compressor-rotors," Applied Energy, Elsevier, vol. 84(10), pages 1012-1027, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tang, Xinzi & Wang, Zhe & Xiao, Peng & Peng, Ruitao & Liu, Xiongwei, 2020. "Uncertainty quantification based optimization of centrifugal compressor impeller for aerodynamic robustness under stochastic operational conditions," Energy, Elsevier, vol. 195(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    2. Jui-Sheng Chou & Dinh-Nhat Truong & Chih-Fong Tsai, 2021. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics," Mathematics, MDPI, vol. 9(6), pages 1-25, March.
    3. Xie, Ying & Zhang, Tao & Liu, Xiaohua, 2016. "Performance investigation of a counter-flow heat pump driven liquid desiccant dehumidification system," Energy, Elsevier, vol. 115(P1), pages 446-457.
    4. Aygun, Hakan & Cilgin, Mehmet Emin & Ekmekci, Ismail & Turan, Onder, 2020. "Energy and performance optimization of an adaptive cycle engine for next generation combat aircraft," Energy, Elsevier, vol. 209(C).
    5. Aygun, Hakan & Kirmizi, Mehmet & Turan, Onder, 2022. "Propeller effects on energy, exergy and sustainability parameters of a small turboprop engine," Energy, Elsevier, vol. 249(C).
    6. Li, Qubo & Piechna, Janusz & Müller, Norbert, 2011. "Numerical simulation of novel axial impeller patterns to compress water vapor as refrigerant," Energy, Elsevier, vol. 36(5), pages 2773-2781.
    7. Li, Qubo & Piechna, Janusz & Müller, Norbert, 2011. "Design of a novel axial impeller as a part of counter-rotating axial compressor to compress water vapor as refrigerant," Applied Energy, Elsevier, vol. 88(9), pages 3156-3168.
    8. Yin, Xiaohong & Wang, Xinli & Li, Shaoyuan & Cai, Wenjian, 2016. "Energy-efficiency-oriented cascade control for vapor compression refrigeration cycle systems," Energy, Elsevier, vol. 116(P1), pages 1006-1019.
    9. Nezamoddini, Nasim & Gholami, Amirhosein & Aqlan, Faisal, 2020. "A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks," International Journal of Production Economics, Elsevier, vol. 225(C).
    10. Cao, Li-hua & Yu, Jing-wen & Li, Yong, 2016. "Study on the determination method of the normal value of relative internal efficiency of the last stage group of steam turbine," Energy, Elsevier, vol. 98(C), pages 101-107.
    11. Mason, Karl & Duggan, Jim & Howley, Enda, 2018. "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, Elsevier, vol. 155(C), pages 705-720.
    12. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    13. Li, Zhihui & Liu, Yanming, 2017. "Blade-end treatment for axial compressors based on optimization method," Energy, Elsevier, vol. 126(C), pages 217-230.
    14. Zheng, Shenglin & Yuan, Rong, 2023. "Sectoral convergence analysis of China's emissions intensity and its implications," Energy, Elsevier, vol. 262(PB).
    15. Singh, Poonam & Pandit, Manjaree & Srivastava, Laxmi, 2023. "Multi-objective optimal sizing of hybrid micro-grid system using an integrated intelligent technique," Energy, Elsevier, vol. 269(C).
    16. Bum Youl Park & Ki-Hyung Lee & Jungsoo Park, 2020. "Conceptual Approach on Feasible Hydrogen Contents for Retrofit of CNG to HCNG under Heavy-Duty Spark Ignition Engine at Low-to-Middle Speed Ranges," Energies, MDPI, vol. 13(15), pages 1-16, July.
    17. Park, Sangjun & Cho, Jungkeun & Park, Jungsoo & Song, Soonho, 2017. "Numerical study of the performance and NOx emission of a diesel-methanol dual-fuel engine using multi-objective Pareto optimization," Energy, Elsevier, vol. 124(C), pages 272-283.
    18. Liu, Junheng & Liang, Wenwen & Ma, Haoran & Ji, Qian & Xiang, Pan & Sun, Ping & Wang, Pan & Wei, Mingliang & Ma, Hongjie, 2023. "Effects of integrated aftertreatment system on regulated and unregulated emission characteristics of non-road methanol/diesel dual-fuel engine," Energy, Elsevier, vol. 282(C).
    19. Heecheong Yoo & Bum Youl Park & Honghyun Cho & Jungsoo Park, 2019. "Performance Optimization of a Diesel Engine with a Two-Stage Turbocharging System and Dual-Loop EGR Using Multi-Objective Pareto Optimization Based on Diesel Cycle Simulation," Energies, MDPI, vol. 12(22), pages 1-26, November.
    20. Aliakbari, Karim & Ebrahimi-Moghadam, Amir & Pahlavanzadeh, Mohammadsadegh & Moradi, Reza, 2023. "Performance characteristics and exhaust emissions of a single-cylinder diesel engine for different fuels: Experimental investigation and artificial intelligence network," Energy, Elsevier, vol. 284(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:106:y:2016:i:c:p:590-601. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.