Skip to main content
Log in

Optimization of Guide Vane Airfoil Shape of Pump Turbine Based on SVM-MDMR Model

  • Research Article-Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Pumped storage is an important green, low-carbon, and clean flexible regulating power source in the power system, which can provide regulation services for the power system, promote the construction of a new type of power system, and facilitate the green transformation of energy. To improve the efficiency and stability of the centrifugal pump turbine under multiple operating conditions, a surrogate model combining radial basis functions with a high-dimensional model is used for performance optimization. Taking the active guide vane of the centrifugal pump turbine as the research object, the airfoil profile is parameterized, and the surrogate model's independent variables and training range are determined. Combining programming and numerical simulation software, an efficiency prediction model for the centrifugal pump and water turbine based on guide vane airfoil control variables is constructed. The particle swarm algorithm is used to globally optimize the constructed model to obtain the optimal efficiency point and corresponding airfoil-related parameters. Finally, numerical simulation and experimental research methods are used to validate the predicted data. The results show that under the premise of ensuring grid performance and operational stability, the numerical simulation efficiency of the pump turbine under the optimization scheme is increased by 1.6 and 0.32%, respectively, compared to the numerical efficiency of the prototype guide vane. In the experimental case, the efficiency of the water turbine and pump is increased by 0.76 and 0.14%, respectively, compared to the prototype guide vane.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data Availability

Data are available upon reasonable request to the authors.

References

  1. Lu, Y.L. et al.: Pumped storage power stations [M]. Water Resources and Electric Power Press (1992)

  2. Cho, J.; Jeong, S.; Kim, Y.: Commercial and research battery technologies for electrical energy storage applications. Prog. Energy Combust. Sci. 48, 84–101 (2015)

    Article  Google Scholar 

  3. Mei, Z.Y.: Pumped Storage Power Generation Technology. Machinery Industry Press (2000)

  4. Cao, S.A.: Pumped Storage Hydropower Plants. Dalian University of Technology Press (2011)

  5. Wang, Q.H.; Zeng, H.; Zhang, Z. et al.: Vibration trend prediction of a pumped storage unit. Hydropower Pumped Storage 7(03), 41–47 (2021)

    Google Scholar 

  6. Chen, X.Y.: Study on the Effect of Guide Vane Airfoil Shape on the "S" Characteristics of Pump-Turbine. Lanzhou University of Science and Technology (2020)

  7. Hu, J.H.: Research on S Characteristics and Pressure Pulsation Control of Pump-Turbine. Wuhan University (2021)

  8. Li, R.N.; Liu, D.X.; Dong, Z.Q. et al.: Numerical simulation of full flow path in the “S” shaped zone of water pump turbine. J. Irrigat. Drainage Mach. Eng. 31(05), 401–405 (2013)

    CAS  Google Scholar 

  9. Hasmatuchi, V.; Roth, S.; Botero, F. et al.: High-speed flow visualization in a pump-turbine under off-design operating conditions. In: 25th IAHR Symposium on Hydraulic Machinery and systems, 12 (2010)

  10. Li, H.B.: Application of pre-opened guide vane method (MGV) in pumped storage power plants. Hydroelectric Technol. 01, 15–16 (2008)

    Google Scholar 

  11. Suh, J.; Yang, H.; Kim, J. et al.: Unstable S-shaped characteristics of a pump-turbine unit in a lab-scale model. Renew. Energy 171, 1395–1417 (2021)

    Article  Google Scholar 

  12. Xiao, R.F.; Sun, H.U.; Liu, W.C. et al.: S-characteristics of pump-turbine under pre-opened guide vane and analysis of pressure pulsation. J. Mec. Eng. 48(08), 174–179 (2012)

    Article  Google Scholar 

  13. Zhao, Y.; Li, D.; Chang, H. et al.: Suppression effect of bionic guide vanes with different parameters on the hump characteristics of pump-turbines based on entropy production theory. Energy, 283 (2023)

  14. Luo, X.Q.; Guo, P.C.; Zhu, G.J. et al.: Multi-objective optimisation design of hydraulic turbine active guide vane based on NSGA-II algorithm. J. Irrigat. Drainage Mach. Eng. 28(05), 369–373 (2010)

    Google Scholar 

  15. Bianco, N.; Fragnito, A.; Iasiello, M.: Multi-objective optimization of a phase change material-based shell-and-tube heat exchanger for cold thermal energy storage: experiments and numerical modeling. Appl. Thermal Eng., 215 (2022)

  16. Seyyedrahmani, F.; Shahabad, P.; Serhat, G.: Multi-objective optimization of composite sandwich panels using lamination parameters and spectral Chebyshev method. Comp. Struct., 289 (2022)

  17. Mosca, V.; Karpuk, S.; Sudhi, A.: Multidisciplinary design optimisation of a fully electric regional aircraft wing with active flow control technology. Aeronaut. J. 126(1298), 730–754 (2022)

    Article  Google Scholar 

  18. Bianco, N.; Fragnito, A.; Iasiello, M.: A CFD multi-objective optimization framework to design a wall-type heat recovery and ventilation unit with phase change material. Appl. Energy, 347 (2023)

  19. Jiang, B.X.; Yang, J.H.; Bai, X.B. et al.: Centrifugal pump blade optimisation based on high-dimensional hybrid model and genetic algorithm. J. Huazhong Univ. Sci. Technol. Nat. Sci. Edn. 48(07), 128–132 (2020)

    Google Scholar 

  20. Jiang, B.X.; Yang, J.H.; Wang, X.H. et al.: Optimisation of hydraulic turbine blades based on RBF-HDMR model and PSO algorithm. J. Mech. Eng. 58(12):283–292 (2022)

  21. Zhang, F.; Fang, M.; Pan, J. et al.: Guide vane profile optimization of pump-turbine for grid connection performance improvement. Energy, 274 (2023)

  22. Tong, L.; Li, G.Y.; Wang, Y.: Kriging-HDMR nonlinear approximate modelling method. J. Mech. 43(04), 780–784 (2011)

    Google Scholar 

  23. Huadi, X.; Zz, C.; Haobo, Q. et al.: Adaptive SVR-HDMR Metamodeling Technique for High Dimensional Problems, Hong Kong, China (2012)

  24. Song, M.: Research on the application of high-dimensional model representation in probabilistic trend and static security domain. Harbin Institute of Technology (2014)

  25. Glaz, B.; Goel, T.; Liu, L. et al.: Multiple-surrogate approach to helicopter rotor blade vibration reduction. AIAA J. 47(1), 271–282 (2009)

    Article  ADS  Google Scholar 

  26. Yang, M.; Li, S.: An efficient implementation of compact third-order implicit reconstruction solver with a simple WBAP limiter for compressible flows on unstructured meshes. Eng. Appl. Comput. Fluid Mech., 17(1) (2023)

  27. Rajput, A.; Sunny, M.R.; Sarkar, A.: Optimization of honeycomb parameters of sandwich composites for energy and specific energy absorption using particle swarm optimization. Mar. Struct., 92 (2023)

  28. Trivedi, C.; Cervantes, M.J.; Gandhi, B.K.: Investigation of a high head francis turbine at runaway operating conditions. Energies, 9(3) (2016)

  29. Yue, N.: Analysis of internal flow and runner dynamics of high head pump turbine based on fluid-solid coupling. Harbin Institute of Technology (2020)

  30. Li, W.; Li, Z.; Han, W. et al.: Measured viscosity characteristics of Fe3O4 ferrofluid in magnetic and thermal fields. Phys. Fluids 35(1), 012002 (2023)

    Article  CAS  ADS  Google Scholar 

  31. Zhang, M.; Montewka, J.; Manderbacka, T. et al.: A big data analytics method for the evaluation of ship—ship collision risk reflecting hydrometeorological conditions. Reliabil. Eng. Syst. Safety, 213 (2021)

  32. Zhao, C.B.: Study on the Stability of Pump-Turbine Flyaway Condition. Lanzhou University of Technology (2019)

  33. Cai, T.T.; Ma, R.: Theoretical foundations of t-SNE for visualizing high -dimensional clustered data. J. Mach. Learn. Res., 23 (2022)

  34. Qu, N.C.; Xu, K.F.; Xiang, L.: Adaptive cavitation flow model based on Omega vortex identification theory. Propul. Technol., pp. 1–16 (2023)

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 52066011: A study of optimization of active guide vanes of pump turbines and the effect of their control theory on internal flow characteristics )

Funding

National Natural Science Foundation of China (52066011).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to this work equally in their respective meaningful ways.

Corresponding author

Correspondence to Qifei Li.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest regarding the presented work and results.

Ethical Approval

Not applicable.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Q., Xin, L., Yao, L. et al. Optimization of Guide Vane Airfoil Shape of Pump Turbine Based on SVM-MDMR Model. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08807-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13369-024-08807-y

Keywords

Navigation