IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v93y2012icp655-667.html
   My bibliography  Save this article

Leading-edge redesign of a turbomachinery blade and its effect on aerodynamic performance

Author

Listed:
  • Zhang, Weihao
  • Zou, Zhengping
  • Ye, Jian

Abstract

A simple and practical polynomial-based turbomachinery profile leading edge geometry design method is proposed in this paper. A mature blade profile – Hodson–Dominy (HD) blade has been selected to verify the effectiveness of the method. Comprehensive analyses on original HD blade and the redesigned blade were conducted by both Large Eddy Simulations (LES) and Reynolds Average Navier–Stokes (RANS) simulations. The results showed that the optimization on HD blade can depress the pressure spike and separation near the leading edge effectively. Meanwhile the separation bubble on suction surface near the trailing edge was reduced partly. The above changes made a 1/3 decrease in suction side leading edge loss and a 10% decrease in the whole profile loss at the design condition. Also, this method was testified to be effective at the conditions with the attack angle from −12° to +9°. Further investigations were carried out on a 5-stage low-pressure turbine, which indicated that the performance was improved in a wide range of working conditions, and a 0.5% increase in efficiency was reported near the design point.

Suggested Citation

  • Zhang, Weihao & Zou, Zhengping & Ye, Jian, 2012. "Leading-edge redesign of a turbomachinery blade and its effect on aerodynamic performance," Applied Energy, Elsevier, vol. 93(C), pages 655-667.
    • Handle: RePEc:eee:appene:v:93:y:2012:i:c:p:655-667
    DOI: 10.1016/j.apenergy.2011.12.091
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191100897X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2011.12.091?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. Hamakhan, I.A. & Korakianitis, T., 2010. "Aerodynamic performance effects of leading-edge geometry in gas-turbine blades," Applied Energy, Elsevier, vol. 87(5), pages 1591-1601, May.
    2. Benini, Ernesto & Biollo, Roberto & Ponza, Rita, 2011. "Efficiency enhancement in transonic compressor rotor blades using synthetic jets: A numerical investigation," Applied Energy, Elsevier, vol. 88(3), pages 953-962, March.
    3. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, July.
    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. Galindo, J. & Fajardo, P. & Navarro, R. & García-Cuevas, L.M., 2013. "Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modeling," Applied Energy, Elsevier, vol. 103(C), pages 116-127.
    2. Gabl, Roman & Innerhofer, Daniel & Achleitner, Stefan & Righetti, Maurizio & Aufleger, Markus, 2018. "Evaluation criteria for velocity distributions in front of bulb hydro turbines," Renewable Energy, Elsevier, vol. 121(C), pages 745-756.
    3. Wang, Xiaojing & Zou, Zhengping, 2019. "Uncertainty analysis of impact of geometric variations on turbine blade performance," Energy, Elsevier, vol. 176(C), pages 67-80.

    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. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
    2. Balerna, Camillo & Lanzetti, Nicolas & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher, 2020. "Optimal low-level control strategies for a high-performance hybrid electric power unit," Applied Energy, Elsevier, vol. 276(C).
    3. Zhang, Yi & Xu, Yujie & Zhou, Xuezhi & Guo, Huan & Zhang, Xinjing & Chen, Haisheng, 2019. "Compressed air energy storage system with variable configuration for accommodating large-amplitude wind power fluctuation," Applied Energy, Elsevier, vol. 239(C), pages 957-968.
    4. Finn, Joshua & Wagner, John & Bassily, Hany, 2010. "Monitoring strategies for a combined cycle electric power generator," Applied Energy, Elsevier, vol. 87(8), pages 2621-2627, August.
    5. Yazar, Isil & Yavuz, Hasan Serhan & Yavuz, Arzu Altin, 2017. "Comparison of various regression models for predicting compressor and turbine performance parameters," Energy, Elsevier, vol. 140(P2), pages 1398-1406.
    6. Lazrak, Amine & Leconte, Antoine & Chèze, David & Fraisse, Gilles & Papillon, Philippe & Souyri, Bernard, 2015. "Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies," Applied Energy, Elsevier, vol. 158(C), pages 142-156.
    7. Li, Zhihui & Liu, Yanming, 2017. "Blade-end treatment for axial compressors based on optimization method," Energy, Elsevier, vol. 126(C), pages 217-230.
    8. Guan, Cong & Theotokatos, Gerasimos & Zhou, Peilin & Chen, Hui, 2014. "Computational investigation of a large containership propulsion engine operation at slow steaming conditions," Applied Energy, Elsevier, vol. 130(C), pages 370-383.
    9. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    10. Bo Li & Chun‐wei Gu, 2016. "Numerical optimization of a highly loaded compressor in semi‐closed cycles using neural networks and genetic algorithms," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 6(2), pages 232-250, April.
    11. Jia-Xuan Liu & Fu-Sheng Yang & Tian-Qing Huo & Jian-Qiang Deng & Zao-Xiao Zhang, 2022. "Analysis of Impact of a Novel Combined Casing Treatment on Flow Characteristics and Performance of a Transonic Compressor," Energies, MDPI, vol. 15(14), pages 1-17, July.
    12. Kumar, Manoj & Behera, Suraj K. & Kumar, Amitesh & Sahoo, Ranjit K., 2019. "Numerical and experimental investigation to visualize the fluid flow and thermal characteristics of a cryogenic turboexpander," Energy, Elsevier, vol. 189(C).
    13. Gabl, Roman & Innerhofer, Daniel & Achleitner, Stefan & Righetti, Maurizio & Aufleger, Markus, 2018. "Evaluation criteria for velocity distributions in front of bulb hydro turbines," Renewable Energy, Elsevier, vol. 121(C), pages 745-756.
    14. Wang, Xiaojing & Zou, Zhengping, 2019. "Uncertainty analysis of impact of geometric variations on turbine blade performance," Energy, Elsevier, vol. 176(C), pages 67-80.
    15. Cortés, O. & Urquiza, G. & Hernández, J.A., 2009. "Optimization of operating conditions for compressor performance by means of neural network inverse," Applied Energy, Elsevier, vol. 86(11), pages 2487-2493, November.
    16. Wei Shao & Shuo Wang & Wenpu Wang & Kun Shao & Qi Xiao & Zheng Cui, 2023. "Experiment and Simulation on a Refrigeration Ventilation System for Deep Metal Mines," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    17. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    18. Zhang, Yi & Xu, Yujie & Guo, Huan & Zhang, Xinjing & Guo, Cong & Chen, Haisheng, 2018. "A hybrid energy storage system with optimized operating strategy for mitigating wind power fluctuations," Renewable Energy, Elsevier, vol. 125(C), pages 121-132.
    19. Likun Ren & Haiqin Qin & Zhenbo Xie & Jing Xie & Bianjiang Li, 2022. "A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    20. Chengtao Wang & Wei Li & Gaifang Xin & Yuqiao Wang & Shaoyi Xu, 2019. "Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network," Complexity, Hindawi, vol. 2019, pages 1-15, October.

    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:appene:v:93:y:2012:i:c:p:655-667. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.