Journal of Mechanical Science and Technology

, Volume 33, Issue 6, pp 2681–2691 | Cite as

Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network

  • Ji Pei
  • Wenjie WangEmail author
  • Majeed Koranteng Osman
  • Xingcheng Gan


To increase efficiency at the design point of a centrifugal pump, this study adopted an artificial neural network in the construction of an accurate nonlinear function between the optimization objective and the design variables of impellers. Modified particle swarm optimization was further applied to refine the mathematical model globally. The database, which consisted of 200 sets of impellers, were generated from the Latin hypercube sampling method, and their corresponding efficiencies were obtained automatically from numerical simulation. Design variables were the distributions of blade angles, and results established that the difference between the numerical performance curve and the experimental results was acceptable. Optimization with a two-layer feedforward network improved the pump efficiency at the design point by 0.454 %. Flow complexity improved as the blade curvature increased. The application of the multilayer neural network could provide a meaningful reference to single- and multi-objective optimization of complex and nonlinear pump performance.


Artificial neural network Centrifugal pump Nonlinear approximate function Numerical simulation Particle swarm optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work is supported by the National Key Research and Development Program (Grant No. 2018YFB0606103), National Natural Science Foundation of China (Grant Nos. 51879121, 51779107), China Postdoctoral Science Foundation funded project (Grant No. 2019M651736), Six Talent Peaks Project (GDZB-047) and Qing Lan Project of Jiangsu Province.


  1. [1]
    J. F. Gülich, Centrifugal Pumps, Berlin: Springer (2008).Google Scholar
  2. [2]
    M. Zangeneh, A. Goto and T. Takemura, Suppression of secondary flows in a mixed-flow pump impeller by application of 3d inverse design method: Part 1—design and numerical valition, ASME 1994 International Gas Turbine and Aeroengine Congress and Exposition, American Society of Mechanical Engineers (1994) V001T01A014-V001T01A014.Google Scholar
  3. [3]
    M. A. A. Chikh et al., Efficiency of bio-and socio-inspired optimization algorithms for axial turbomachinery design, Applied Soft Computing, 64 (2018) 282–306CrossRefGoogle Scholar
  4. [4]
    N. Timnak and A. Jahangirian, Multi-point optimization of transonic airfoils using an enhanced genetic algorithm, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 232 (7) (2018) 1347–1360CrossRefGoogle Scholar
  5. [5]
    A. Oyama, M. S. Liou and S. Obayashi, Transonic axial-flow blade optimization: Evolutionary algorithms/three-dimensional Navier-Stokes solver, Journal of Propulsion and Power, 20 (4) (2004) 612–619CrossRefGoogle Scholar
  6. [6]
    W. Wahba and A. A. Tourlidakis, Genetic algorithm applied to the design of blade profiles for centrifugal pump impellers, 15th AIAA Computational Fluid Dynamics Conference (2001) 2582.Google Scholar
  7. [7]
    M. A. Bezerra et al., Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta, 76 (5) (2008) 965–977MathSciNetCrossRefGoogle Scholar
  8. [8]
    J. P. C. Kleijnen, Kriging metamodeling in simulation: A review, European Journal of Operational Research, 192 (3) (2009) 707–716MathSciNetCrossRefzbMATHGoogle Scholar
  9. [9]
    N. Murata, S. Yoshizawa and S. Amari, Network information criterion-determining the number of hidden units for an artificial neural network model, IEEE Transactions on Neural Networks, 5 (6) (1994) 865–872CrossRefGoogle Scholar
  10. [10]
    M. S. Cao et al., Neural network ensemble-based parameter sensitivity analysis in civil engineering systems, Neural Computing and Applications, 28 (7) (2017) 1583–1590CrossRefGoogle Scholar
  11. [11]
    L. Darvishvand, B. Kamkari and F. Kowsary, Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method, Engineering Optimization, 50 (3) (2018) 452–470MathSciNetCrossRefGoogle Scholar
  12. [12]
    S. Chatterjee et al., Particle swarm optimization trained neural network for structural failure prediction of multisto-ried RC buildings, Neural Computing and Applications, 28 (8) (2017) 2005–2016CrossRefGoogle Scholar
  13. [13]
    S. Derakhshan et al., Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm, Computers & Fluids, 81 (2013) 145–151CrossRefGoogle Scholar
  14. [14]
    B. Duan et al., Multi-objective hydraulic optimization and analysis in a minipump, Science Bulletin, 60 (17) (2015) 1517–1526CrossRefGoogle Scholar
  15. [15]
    H. S. Shim, K. Y. Kim and Y. S. Choi, Three-objective optimization of a centrifugal pump to reduce flow recirculation and cavitation, Journal of Fluids Engineering, 140 (9) (2018) 091202.CrossRefGoogle Scholar
  16. [16]
    J. W. Suh et al., A study on numerical optimization and performance verification of multiphase pump for offshore plant, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 231 (5) (2017) 382–397Google Scholar
  17. [17]
    J. W. Suh et al., Multi-objective optimization of the hydro-dynamic performance of the second stage of a multi-phase pump, Energies, 10 (9) (2017) 1334.CrossRefGoogle Scholar
  18. [18]
    F. Miao et al., Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller, Journal of Mechanical Science and Technology, 29 (11) (2015) 4867–4876CrossRefGoogle Scholar
  19. [19]
    J. Zhang et al., Multi-objective shape optimization of helico-axial multiphase pump impeller based on NSGA-II and ANN, Energy Conversion and Management, 52 (1) (2011) 538–546CrossRefGoogle Scholar
  20. [20]
    A. Goto, Historical perspective on fluid machinery flow optimization in an industry, International Journal of Fluid Machinery and Systems, 9 (1) (2016) 75–84CrossRefGoogle Scholar
  21. [21]
    R. C. Eberhart and J. Kennedy, Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, 4 (1995) 1942–1948CrossRefGoogle Scholar
  22. [22]
    S. K. S. Fan and E. Zahara, A hybrid simplex search and particle swarm optimization for unconstrained optimization, European Journal of Operational Research, 181 (2) (2007) 527–548MathSciNetCrossRefzbMATHGoogle Scholar
  23. [23]
    K. E. Parsopoulos and M. N. Vrahatis, Particle swarm optimization method for constrained optimization problems, intelligent technologies–Theory and application, New Trends in Intelligent Technologies, 76 (1) (2002) 214–220Google Scholar
  24. [24]
    G. Haisi, Numerical and Experimental Investigation on the Influence of Blade Trailing Edge Profile on Internal Flow Characteristics in Centrifugal Pump, Jiangsu University (2017).Google Scholar
  25. [25]
    F. R. Menter, Review of the shear-stress transport turbulence model experience from an industrial perspective, International Journal of Computational Fluid Dynamics, 23 (4) (2009) 305–316CrossRefzbMATHGoogle Scholar
  26. [26]
    G. Ardizzon, G. Cavazzini and G. Pavesi, Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms, Information Sciences, 299 (2015) 337–378CrossRefGoogle Scholar

Copyright information

© KSME & Springer 2019

Authors and Affiliations

  • Ji Pei
    • 1
  • Wenjie Wang
    • 1
    Email author
  • Majeed Koranteng Osman
    • 1
    • 2
  • Xingcheng Gan
    • 1
  1. 1.National Research Center of PumpsJiangsu UniversityZhenjiangChina
  2. 2.Mechanical Engineering DepartmentWa PolytechnicWaGhana

Personalised recommendations