IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v87y2010i5p1591-1601.html
   My bibliography  Save this article

Aerodynamic performance effects of leading-edge geometry in gas-turbine blades

Author

Listed:
  • Hamakhan, I.A.
  • Korakianitis, T.

Abstract

The purpose of this paper is to illustrate the advantages of the direct surface-curvature distribution blade-design method, originally proposed by Korakianitis, for the leading-edge design of turbine blades, and by extension for other types of airfoil shapes. The leading edge shape is critical in the blade design process, and it is quite difficult to completely control with inverse, semi-inverse or other direct-design methods. The blade-design method is briefly reviewed, and then the effort is concentrated on smoothly blending the leading edge shape (circle or ellipse, etc.) with the main part of the blade surface, in a manner that avoids leading-edge flow-disturbance and flow-separation regions. Specifically in the leading edge region we return to the second-order (parabolic) construction line coupled with a revised smoothing equation between the leading-edge shape and the main part of the blade. The Hodson-Dominy blade has been used as an example to show the ability of this blade-design method to remove leading-edge separation bubbles in gas turbine blades and other airfoil shapes that have very sharp changes in curvature near the leading edge. An additional gas turbine blade example has been used to illustrate the ability of this method to design leading edge shapes that avoid leading-edge separation bubbles at off-design conditions. This gas turbine blade example has inlet flow angle 0°, outlet flow angle -64.3°, and tangential lift coefficient 1.045, in a region of parameters where the leading edge shape is critical for the overall blade performance. Computed results at incidences of -10°,  -5°,  +5°,  +10° are used to illustrate the complete removal of leading edge flow-disturbance regions, thus minimizing the possibility of leading-edge separation bubbles, while concurrently minimizing the stagnation pressure drop from inlet to outlet. These results using two difficult example cases of leading edge geometries illustrate the superiority and utility of this blade-design method when compared with other direct or inverse blade-design methods.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:5:p:1591-1601
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(09)00396-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Ghigliazza, Francesco & Traverso, Alberto & Massardo, Aristide Fausto, 2009. "Thermoeconomic impact on combined cycle performance of advanced blade cooling systems," Applied Energy, Elsevier, vol. 86(10), pages 2130-2140, October.
    2. Lebele-Alawa, B.T. & Hart, H.I. & Ogaji, S.O.T. & Probert, S.D., 2008. "Rotor-blades' profile influence on a gas-turbine's compressor effectiveness," Applied Energy, Elsevier, vol. 85(6), pages 494-505, June.
    3. Fast, M. & Assadi, M. & De, S., 2009. "Development and multi-utility of an ANN model for an industrial gas turbine," Applied Energy, Elsevier, vol. 86(1), pages 9-17, January.
    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. 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.
    2. 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. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    2. Asif Afzal & Saad Alshahrani & Abdulrahman Alrobaian & Abdulrajak Buradi & Sher Afghan Khan, 2021. "Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms," Energies, MDPI, vol. 14(21), pages 1-22, November.
    3. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    4. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).
    5. Nikpey, H. & Assadi, M. & Breuhaus, P., 2013. "Development of an optimized artificial neural network model for combined heat and power micro gas turbines," Applied Energy, Elsevier, vol. 108(C), pages 137-148.
    6. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    7. Park, Yeseul & Choi, Minsung & Choi, Gyungmin, 2022. "Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method," Energy, Elsevier, vol. 251(C).
    8. Sipöcz, Nikolett & Tobiesen, Finn Andrew & Assadi, Mohsen, 2011. "The use of Artificial Neural Network models for CO2 capture plants," Applied Energy, Elsevier, vol. 88(7), pages 2368-2376, July.
    9. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(C).
    10. 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.
    11. Homam Nikpey Somehsaraei & Susmita Ghosh & Sayantan Maity & Payel Pramanik & Sudipta De & Mohsen Assadi, 2020. "Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning," Energies, MDPI, vol. 13(14), pages 1-15, July.
    12. Fang, Xiande & Dai, Qiumin & Yin, Yanxin & Xu, Yu, 2010. "A compact and accurate empirical model for turbine mass flow characteristics," Energy, Elsevier, vol. 35(12), pages 4819-4823.
    13. Rahmoune, Mohamed Ben & Hafaifa, Ahmed & Kouzou, Abdellah & Chen, XiaoQi & Chaibet, Ahmed, 2021. "Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 23-47.
    14. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    15. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    16. Xu, Xiandong & Li, Kang & Qi, Fengyu & Jia, Hongjie & Deng, Jing, 2017. "Identification of microturbine model for long-term dynamic analysis of distribution networks," Applied Energy, Elsevier, vol. 192(C), pages 305-314.
    17. Feneley, Adam J. & Pesiridis, Apostolos & Andwari, Amin Mahmoudzadeh, 2017. "Variable Geometry Turbocharger Technologies for Exhaust Energy Recovery and Boosting‐A Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 959-975.
    18. Fang, Xiande & Xu, Yu, 2011. "Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis," Energy, Elsevier, vol. 36(5), pages 2937-2942.
    19. Kim, Sangjo & Kim, Kuisoon & Son, Changmin, 2020. "Transient system simulation for an aircraft engine using a data-driven model," Energy, Elsevier, vol. 196(C).
    20. Salta, Myrsine & Polatidis, Heracles & Haralambopoulos, Dias, 2011. "Industrial combined heat and power (CHP) planning: Development of a methodology and application in Greece," Applied Energy, Elsevier, vol. 88(5), pages 1519-1531, May.

    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:87:y:2010:i:5:p:1591-1601. 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.