IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v220y2021ics0360544220328589.html
   My bibliography  Save this article

A blackbox optimization of volumetric heating rate for reducing the wetness of the steam flow through turbine blades

Author

Listed:
  • Hoseinzade, Davood
  • Lakzian, Esmail
  • Hashemian, Ali

Abstract

This paper proposes to use a blackbox optimization to obtain the optimal volumetric heating required to reduce the wetness at the last stages of steam turbines. For this purpose, a global multiobjective optimization is utilized through the automatic linking of genetic algorithm and CFD code, where the blackbox function evaluations are performed by CFD runs. The logarithm of number of droplets per volume (LND), the droplet average radius (DAR), and the integral of local entropy (ILE) at the end of the cascade (after the condensation location) are minimized, while the volumetric heating rate is the optimization parameter. The Eulerian–Eulerian approach is implemented to model the two-phase wet steam turbulent flow and the numerical results are validated against well-established experiments. Since higher volumetric heating rates reduce DAR and LND, while increase ILE, according to optimization results, there is an optimum for the volumetric heating rate to reach the best performance of steam turbines. For case studies presented in this work, the optimal volumetric heating rates of 5.21×108 and 4.67×108 W/m2 are obtained for two different cases of supersonic and subsonic outlets, respectively. Particularly, these rates improve DAR by 45.7% and 57.5%, and LND by 6.0% and 7.8% for respective cases.

Suggested Citation

  • Hoseinzade, Davood & Lakzian, Esmail & Hashemian, Ali, 2021. "A blackbox optimization of volumetric heating rate for reducing the wetness of the steam flow through turbine blades," Energy, Elsevier, vol. 220(C).
  • Handle: RePEc:eee:energy:v:220:y:2021:i:c:s0360544220328589
    DOI: 10.1016/j.energy.2020.119751
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220328589
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.119751?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. Mirhoseini, Mohadeseh Sadat & Boroomand, Masoud, 2017. "Multi-objective optimization of hot steam injection variables to control wetness parameters of steam flow within nozzles," Energy, Elsevier, vol. 141(C), pages 1027-1037.
    2. Rommel Regis & Christine Shoemaker, 2005. "Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions," Journal of Global Optimization, Springer, vol. 31(1), pages 153-171, January.
    3. Vatanmakan, Masoud & Lakzian, Esmail & Mahpeykar, Mohammad Reza, 2018. "Investigating the entropy generation in condensing steam flow in turbine blades with volumetric heating," Energy, Elsevier, vol. 147(C), pages 701-714.
    4. Wróblewski, Włodzimierz & Dykas, Sławomir, 2016. "Two-fluid model with droplet size distribution for condensing steam flows," Energy, Elsevier, vol. 106(C), pages 112-120.
    5. Aliabadi, Mohammad Ali Faghih & Lakzian, Esmail & Khazaei, Iman & Jahangiri, Ali, 2020. "A comprehensive investigation of finding the best location for hot steam injection into the wet steam turbine blade cascade," Energy, Elsevier, vol. 190(C).
    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. Ohiemi, Israel Enema & Sunsheng, Yang & Singh, Punit & Li, Yanjun & Osman, Fareed, 2023. "Evaluation of energy loss in a low-head axial flow turbine under different blade numbers using entropy production method," Energy, Elsevier, vol. 274(C).
    2. Zhang, Guojie & Wang, Xiaogang & Wiśniewski, Piotr & Chen, Jiaheng & Qin, Xiang & Dykas, Sławomir, 2023. "Effect of NaCl presence caused by salting out on the heterogeneous-homogeneous coupling non-equilibrium condensation flow in a steam turbine cascade," Energy, Elsevier, vol. 263(PE).
    3. Hu, Pengfei & Zhao, Pu & Li, Qi & Hou, Tianbo & Wang, Shibo & Cao, Lihua & Wang, Yanhong, 2023. "Performance of non-equilibrium condensation flow in wet steam zone of steam turbine based on modified model," Energy, Elsevier, vol. 267(C).
    4. Ansari, Mehran & Esfahanian, Vahid & Izadi, Mohammad Javad & Bashi, Hosein & Tavakoli, Alireza & Kordi, Mohammad, 2023. "Implementation of hot steam injection in steam turbine design: A novel mean-line method coupled with multi-objective optimization and neural network," Energy, Elsevier, vol. 283(C).
    5. Dolatabadi, Amir Momeni & Lakzian, Esmail & Heydari, Mahdi & Khan, Afrasyab, 2022. "A modified model of the suction technique of wetness reducing in wet steam flow considering power-saving," Energy, Elsevier, vol. 238(PA).
    6. Zhang, Guojie & Wang, Xiaogang & Chen, Jiaheng & Tang, Songzhen & Smołka, Krystian & Majkut, Mirosław & Jin, Zunlong & Dykas, Sławomir, 2023. "Supersonic nozzle performance prediction considering the homogeneous-heterogeneous coupling spontaneous non-equilibrium condensation," Energy, Elsevier, vol. 284(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. Aliabadi, Mohammad Ali Faghih & Lakzian, Esmail & Khazaei, Iman & Jahangiri, Ali, 2020. "A comprehensive investigation of finding the best location for hot steam injection into the wet steam turbine blade cascade," Energy, Elsevier, vol. 190(C).
    2. Ansari, Mehran & Esfahanian, Vahid & Izadi, Mohammad Javad & Bashi, Hosein & Tavakoli, Alireza & Kordi, Mohammad, 2023. "Implementation of hot steam injection in steam turbine design: A novel mean-line method coupled with multi-objective optimization and neural network," Energy, Elsevier, vol. 283(C).
    3. Zhang, Guojie & Zhang, Xinzhe & Wang, Fangfang & Wang, Dingbiao & Jin, Zunlong & Zhou, Zhongning, 2019. "Design and optimization of novel dehumidification strategies based on modified nucleation model in three-dimensional cascade," Energy, Elsevier, vol. 187(C).
    4. Han, Xu & Zeng, Wei & Han, Zhonghe, 2019. "Investigation of the comprehensive performance of turbine stator cascades with heating endwall fences," Energy, Elsevier, vol. 174(C), pages 1188-1199.
    5. Momeni Dolatabadi, Amir & Moslehi, Jamshid & Saffari Pour, Mohsen & Mousavi Ajarostaghi, Seyed Soheil & Poncet, Sébastien & Arıcı, Müslüm, 2022. "Modified model of reduction condensing losses strategy into the wet steam flow considering efficient energy of steam turbine based on injection of nano-droplets," Energy, Elsevier, vol. 242(C).
    6. Dolatabadi, Amir Momeni & Lakzian, Esmail & Heydari, Mahdi & Khan, Afrasyab, 2022. "A modified model of the suction technique of wetness reducing in wet steam flow considering power-saving," Energy, Elsevier, vol. 238(PA).
    7. Zhonghe Han & Wei Zeng & Xu Han & Peng Xiang, 2018. "Investigating the Dehumidification Characteristics of Turbine Stator Cascades with Parallel Channels," Energies, MDPI, vol. 11(9), pages 1-17, September.
    8. Hu, Pengfei & Zhao, Pu & Li, Qi & Hou, Tianbo & Wang, Shibo & Cao, Lihua & Wang, Yanhong, 2023. "Performance of non-equilibrium condensation flow in wet steam zone of steam turbine based on modified model," Energy, Elsevier, vol. 267(C).
    9. Bian, Jiang & Cao, Xuewen & Yang, Wen & Edem, Mawugbe Ayivi & Yin, Pengbo & Jiang, Wenming, 2018. "Supersonic liquefaction properties of natural gas in the Laval nozzle," Energy, Elsevier, vol. 159(C), pages 706-715.
    10. Rommel G. Regis & Christine A. Shoemaker, 2009. "Parallel Stochastic Global Optimization Using Radial Basis Functions," INFORMS Journal on Computing, INFORMS, vol. 21(3), pages 411-426, August.
    11. Lehmann, Sebastian & Huth, Andreas, 2015. "Fast calibration of a dynamic vegetation model with minimum observation data," Ecological Modelling, Elsevier, vol. 301(C), pages 98-105.
    12. Krityakierne, Tipaluck & Baowan, Duangkamon, 2020. "Aggregated GP-based Optimization for Contaminant Source Localization," Operations Research Perspectives, Elsevier, vol. 7(C).
    13. Jesús Martínez-Frutos & David Herrero-Pérez, 2016. "Kriging-based infill sampling criterion for constraint handling in multi-objective optimization," Journal of Global Optimization, Springer, vol. 64(1), pages 97-115, January.
    14. Driessen, L. & Brekelmans, R.C.M. & Gerichhausen, M. & Hamers, H.J.M. & den Hertog, D., 2006. "Why Methods for Optimization Problems with Time-Consuming Function Evaluations and Integer Variables Should Use Global Approximation Models," Other publications TiSEM 45a73d28-9fed-4b4c-a909-1, Tilburg University, School of Economics and Management.
    15. Alberto Bemporad, 2020. "Global optimization via inverse distance weighting and radial basis functions," Computational Optimization and Applications, Springer, vol. 77(2), pages 571-595, November.
    16. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    17. Juliane Müller & Robert Piché, 2011. "Mixture surrogate models based on Dempster-Shafer theory for global optimization problems," Journal of Global Optimization, Springer, vol. 51(1), pages 79-104, September.
    18. Zan Yang & Haobo Qiu & Liang Gao & Chen Jiang & Jinhao Zhang, 2019. "Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems," Journal of Global Optimization, Springer, vol. 74(2), pages 327-359, June.
    19. Juliane Müller & Christine Shoemaker, 2014. "Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems," Journal of Global Optimization, Springer, vol. 60(2), pages 123-144, October.
    20. Cao, Lihua & Li, Longge & Dong, Enfu & Si, Heyong & Ning, Zhe & Liu, Miao, 2019. "Influence of aerodynamic characteristics optimization of exhaust passage on heat transfer of condenser in steam turbine," Energy, Elsevier, vol. 188(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:220:y:2021:i:c:s0360544220328589. 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.