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

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  • 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.

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  • 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
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    References listed on IDEAS

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    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).
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    Citations

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    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).

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