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Multi-component optimization of a vertical inline pump based on multi-objective pso and artificial neural network

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Abstract

The vertical inline pump is a single-stage single-suction centrifugal pump with a curved inlet pipe before the impeller, which is widely used in where the constraint is installation space. In this paper, with the objective functions of efficiencies at 0.5Qd, 1.0Qd, and 1.5Qd, a multi-objective optimization on inlet pipe and impeller was proposed to broaden the efficient operating period of a vertical inline pump. Two 5th order Bézier curves were adopted to fit the shape of the mid curve of the inlet pipe and the trend of the blade angle of the impeller. Fourteen design variables were selected after the data-mining process. 300 sample cases were generated using Latin hypercube sampling (LHS), and they were solved by 3D RANS code to obtain the objective functions. The feed-forward artificial neural network with a hidden layer and an output layer was adopted to fit the two objective functions and the 14 design variables. The Pareto frontiers were generated for the three objectives using multi-objective particle swarm optimization (MOPSO), and three different configurations on the Pareto front are selected for detailed study by computational fluid dynamics (CFD). The results showed that the profile of the inlet pipe and the blade have a dramatic impact on the performance of the vertical inline pump. The Pareto frontiers reported that the performance under the overload condition usually keeps stable when the nominal efficiency is lower than 82 %, or the part-load efficiency is lower than 62 %, and it will decrease rapidly after that. After optimization, the improvement of efficiencies at the part-load condition and nominal condition of the picked case were 9.65 % and 7.95 %, respectively.

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Abbreviations

b 1 :

Impeller inlet width, mm

b 2 :

Impeller outlet width, mm

D 1 :

The diameter of impeller inlet, mm

D s :

The diameter of the suction pipe, mm

D d :

The diameter of the delivery pipe, mm

H :

Pump head, m

n :

Rotating speed of impeller, rpm

n s :

Specific speed of the pump

Q :

Flow rate, m3/s

Q d :

The volume flow rate of design flow condition, m3/h

u 2 :

Impeller peripheral velocity at the outlet, m/s

x i :

Horizontal coordinate of control point i, mm

y i :

Vertical coordinate of control point i, mm

z :

Number of blades

β 1 :

Impeller inlet vane angle, degree

β 2 :

Impeller outlet vane angle, degree

η :

The efficiency of the pump

ϕ :

Flow coefficient

ψ :

Head coefficient

C p :

Pressure coefficient

References

  1. C. Stephen et al., Computational study of fluid borne noise in vertical inline pump, Journal of Drainage and Irrigation Machinery Engineering, 37 (2) (2019) 93–99.

    Google Scholar 

  2. C. Stephen, S. Yuan, J. Pei and X. Gan, Numerical flow prediction in inlet pipe of vertical inline pump, ASME J. Fluids Eng., 140 (5) (2018) 051201.

    Article  Google Scholar 

  3. F. Zhang et al., Energy loss evaluation in a side channel pump under different wrapping angles using entropy production method, International Communications in Heat and Mass Transfer, 113 (2020).

  4. Y. Wang et al., Effect of unrans and hybrid rans-les turbulence models on unsteady turbulent flows inside a side channel pump, ASME Journal of Fluids Engineering (2020).

  5. G. G. Wang and S. Shan, Review of metamodeling techniques in support of engineering design optimization, Journal of Mechanical Design, 129 (4) (2007) 370–380.

    Article  Google Scholar 

  6. W. Wang et al., Application of different surrogate models on the optimization of centrifugal pump, Journal of Mechanical Science and Technology, 30 (2) (2016) 567–574.

    Article  Google Scholar 

  7. J. Pei et al., Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network, Journal of Mechanical Science and Technology, 33 (6) (2019) 2681–2691.

    Article  Google Scholar 

  8. J. Pei, Multi-point optimization on meridional shape of a centrifugal pump impeller for performance improvement, Journal of Mechanical Science and Technology, 30 (11) (2016) 4949–4960.

    Article  Google Scholar 

  9. J. H. Kim and K. Y. Kim, Analysis and optimization of a vaned diffuser in a mixed flow pump to improve hydrody-namic performance, Journal of Fluids Engineering, 134 (7) (2012) 071104.

    Article  Google Scholar 

  10. J. H. Kim, J. H. Choi and K. Y. Kim, Design optimization of a centrifugal compressor impeller using radial basis neural network method, ASME Turbo Expo 2009: Power for Land, Sea, and Air, American Society of Mechanical Engineers (2009) 443–451.

  11. S. Derakhshan et al., Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm, Computers & Fluids, 81 (2013) 145–151.

    Article  Google Scholar 

  12. A. Nourbakhsh, H. Safikhani and S. Derakhshan, The comparison of multi-objective particle swarm optimization and NSGA II algorithm: Applications in centrifugal pumps, Engineering Optimization, 43 (10) (2011) 1095–1113.

    Article  MathSciNet  Google Scholar 

  13. S. Pierret and R. A. Van den Braembussche, Turbomachinery blade design using a Navier-Stokes solver and artificial neural network, ASME 1998 International Gas Turbine and Aeroengine Congress and Exhibition, American Society of Mechanical Engineers (1998).

  14. A. Demeulenaere, A. Ligout and C. Hirsch, Application of multipoint optimization to the design of turbomachinery blades, ASME Turbo Expo 2004: Power for Land, Sea, and Air, American Society of Mechanical Engineers (2004) 1481–1489.

  15. T. Mengistu and W. Ghaly, Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models, Optimization and Engineering, 9 (3) (2008) 239–255.

    Article  MathSciNet  MATH  Google Scholar 

  16. C. M. Jang and K. Y. Kim, Optimization of a stator blade using response surface method in a single-stage transonic axial compressor, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 219 (8) (2005) 595–603.

    Article  Google Scholar 

  17. D. Buche, G. Guidati and P. Stoll, Automated design optimization of compressor blades for stationary, large-scale turbomachinery, ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference, American Society of Mechanical Engineers (2003) 1249–1257.

  18. J. I. Madsen, W. Shyy and R. T. Haftka, Response surface techniques for diffuser shape optimization, AIAA Journal, 38 (9) (2000) 1512–1518.

    Article  Google Scholar 

  19. J. Pei et al., Multi-objective shape optimization on the inlet pipe of a vertical inline pump, Journal of Fluids Engineering, 141 (6) (2019).

  20. X. Gan et al., Direct shape optimization and parametric analysis of a vertical inline pump via multi-objective particle swarm optimization, Energies, 13 (2) (2020) 425.

    Article  Google Scholar 

  21. M. D. McKay, R. J. Beckman and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 42 (2000) 55–61.

    Article  MATH  Google Scholar 

  22. W. McCulloch and W. Pitts, A logical calculus of ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 5 (4) (1943) 115–133.

    Article  MathSciNet  MATH  Google Scholar 

  23. X. Gan et al., Multi-objective optimization on inlet pipe of a vertical inline pump based on genetic algorithm and artificial neural network, ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting, American Society of Mechanical Engineers (2018).

  24. R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science (1995).

  25. Z. Li and X. Zheng, Review of design optimization methods for turbomachinery aerodynamics, Progress in Aerospace Sciences, 93 (2017) 1–23.

    Article  Google Scholar 

  26. Y. Shi, Particle swarm optimization: Developments, applications and resources, Evol. Comput., 1 (2001) 81–86.

    Google Scholar 

  27. R. C. Eberhart and Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, Evol. Comput., 1 (2000) 84–88.

    Google Scholar 

  28. M. S. Khurana, H. Winarto and A. K. Sinha, Application of swarm approach and artificial neural networks for airfoil shape optimization, 12thAIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (2008).

  29. M. Pontani and B. A. Conway, Particle swarm optimization applied to space trajectories, J. Guid. Control. Dynam., 33 (2010) 1429–1441.

    Article  Google Scholar 

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Acknowledgments

This work is funded by the National Natural Science Foundation of China (Grant No. 51879121), Natural Science Foundation of Jiangsu Province (Grant No. BK20190851), China Postdoctoral Science Foundation funded project (Grant No. 2019M651736, 2019T120394), Primary Research & Development Plan of Jiangsu Province (Grant No. BE2019009-1), Open Research Subject of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education (szjj2019-007).

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Authors and Affiliations

  1. National Research Center of Pumps, Jiangsu University, Zhenjiang, Jiangsu, China

    Xingcheng Gan, Ji Pei, Wenjie Wang, Shouqi Yuan & Yajing Tang

Authors
  1. Xingcheng Gan
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  2. Ji Pei
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  3. Wenjie Wang
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  4. Shouqi Yuan
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  5. Yajing Tang
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Corresponding author

Correspondence to Ji Pei.

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Recommended by Guest Editor Seung Jin Song

Xingcheng Gan is currently a Ph.D. candidate in the National Research Center of Pumps, Jiangsu University. He received his MA degree from Jiangsu University in 2019. His research interests include the optimi-zation design, computational flow dynamics and analysis of unsteady flow of centrifugal pump.

Ji Pei is currently an Associate Professor in National Research Center of Pumps, Jiangsu University. He received his Ph.D. degree from Jiangsu University in 2013. His research interests include unsteady flow, flow-induced vibration, and fluid-structure interaction in turbomachinery.

Wenjie Wang is currently an Assistant Professor in the National Research Center of Pumps, Jiangsu University. He received his Ph.D. degree from Jiangsu University in 2017. His research interests include the optimization design and analysis of unsteady flow of centrifugal pump.

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Gan, X., Pei, J., Wang, W. et al. Multi-component optimization of a vertical inline pump based on multi-objective pso and artificial neural network. J Mech Sci Technol 34, 4883–4896 (2020). https://doi.org/10.1007/s12206-020-2101-4

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  • DOI: https://doi.org/10.1007/s12206-020-2101-4

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