DOI QR코드

DOI QR Code

Optimization of a Single-Channel Pump Impeller for Wastewater Treatment

  • Kim, Joon-Hyung (Thermal & Fluid System R&D Group, Korea Institute of Industrial Technology) ;
  • Cho, Bo-Min (Thermal & Fluid System R&D Group, Korea Institute of Industrial Technology) ;
  • Kim, Youn-Sung (Department of Mechanical Engineering, Inha University) ;
  • Choi, Young-Seok (Thermal & Fluid System R&D Group, Korea Institute of Industrial Technology) ;
  • Kim, Kwang-Yong (Department of Mechanical Engineering, Inha University) ;
  • Kim, Jin-Hyuk (Thermal & Fluid System R&D Group, Korea Institute of Industrial Technology) ;
  • Cho, Yong (K-water Institute, Korea Water Resources Corperation)
  • Received : 2016.05.10
  • Accepted : 2016.10.17
  • Published : 2016.12.31

Abstract

As a single-channel pump is used for wastewater treatment, this particular pump type can prevent performance reduction or damage caused by foreign substances. However, the design methods for single-channel pumps are different and more difficult than those for general pumps. In this study, a design optimization method to improve the hydrodynamic performance of a single-channel pump impeller is implemented. Numerical analysis was carried out by solving three-dimensional steady-state incompressible Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. As a state-of-the-art impeller design method, two design variables related to controlling the internal cross-sectional flow area of a single-channel pump impeller were selected for optimization. Efficiency was used as the objective function and was numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. An optimization process based on a radial basis neural network model was conducted systematically, and the performance of the optimum model was finally evaluated through an experimental test. Consequently, the optimum model showed improved performance compared with the base model, and the unstable flow components previously observed in the base model were suppressed remarkably well.

Keywords

Acknowledgement

Supported by : Korea Institute of Industrial Technology (KITECH)

References

  1. Zhang H, Chen B, Shi WD, Pan ZY, Cao WD, 2014, "Effects of contraction-type impeller on non-overloaded performance for low-specific-speed sewage pump," Journal of Mechanical Science and Technology, Vol. 28, No. 3, pp. 937-944. https://doi.org/10.1007/s12206-013-1165-9
  2. Isono M, Nohmi M, Uchida H, Kawai M, Kudo H, Kawahara T, et al., 2014. "An experimental study on pump clogging. In IOP Conference Series," Earth and Environmental Science, Vol. 22, No. 1, p. 012009.
  3. Corwin BJ, Nation M. 2011, "New Pumps Solve Ragging Problems," Proceedings of the Water Environment Federation, Vol. 2011, No. 5, pp. 388-394.
  4. Pei J, Benra FK, Dohmen HJ, 2012, "Application of different strategies of partitioned fluid-structure interaction simulation for a single-blade pump impeller," Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, Vol. 226, No. 4, pp. 297-308. https://doi.org/10.1177/0954408911432974
  5. Keays J, Meskell C, 2006, "A study of the behaviour of a single-bladed waste-water pump," Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, Vol. 220, No. 2, pp. 79-87. https://doi.org/10.1243/09544089JPME60
  6. Hansen BF, Henning PJ, 2013, "Waste water pump," U.S. Patent Application, Vol. 13, No. 886, p. 479.
  7. Kim JH, Ovgor B, Cha KH, Kim JH, Lee S, Kim KY, 2014, "Optimization of the aerodynamic and aeroacoustic performance of an axial-flow fan," AIAA Journal, Vol. 52, No. 9, pp. 2032-2043. https://doi.org/10.2514/1.J052754
  8. Derakhshan S, Pourmahdavi M, Abdolahnejad E, Reihani A, Ojaghi A, 2013, "Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm," Computers & Fluids, Vol. 81, pp. 141-151.
  9. Safikhani H, Khalkhali A, Farajpoor M, 2011, "Pareto based multi-objective optimization of centrifugal pumps using CFD, neural networks and genetic algorithms," Journal of Hydraulic Research, Vol. 5, No. 1, pp. 37-48.
  10. Kim JH, Kim KY, 2011, "Optimization of Vane Diffuser in a Mixed-Flow Pump for High Efficiency Design," International Journal of Fluid Machinery and Systems, Vol. 4, No. 1, pp. 172-178. https://doi.org/10.5293/IJFMS.2011.4.1.172
  11. Nourbakhsh A, Safikhani H, Derakhshan S, 2011, "The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps," Journal of Hydraulic Research, Vol. 43, No. 10, pp. 1095-1113.
  12. Anagnostopoulos JS, 2009, "A fast numerical method for flow analysis and blade design in centrifugal pump impellers," Computers & Fluids, Vol. 38, No. 2, pp. 284-289. https://doi.org/10.1016/j.compfluid.2008.02.010
  13. Kim JH, Lee HC, Kim JH, Choi YS, Yoon JY, Yoo IS, Choi WC, 2012, "Improvement of hydrodynamic performance of a multiphase pump using design of experiment techniques," Journal of Fluids Engineering, Vol. 137, No. 8, p. 081301. https://doi.org/10.1115/1.4029890
  14. Kelder JDH, Dijkers RJH, Van Esch BPM, Kruyt NP, 2001, "Experimental and theoretical study of the flow in the volute of a low specific-speed pump", Fluid Dynamics Research, Vol. 28, No. 4, pp. 267-280. https://doi.org/10.1016/S0169-5983(00)00032-0
  15. ANSYS CFX, ANSYS CFX-Solver Theory Guide, ANSYS Inc, 2009, No. 12, pp. 1-270.
  16. Eymard R, Gallouet T, Herbin R, 2000, "Finite volume methods," Handbook of numerical analysis. Vol. 7, pp. 713-1018.
  17. Vaz G, Waals OJ, Ottens H, Fathi F, Le Souef T, Kiu K, 2009, "Current Affairs: Model Tests, Semi-Empirical Predictions and CFD Computations for Current Coefficients of Semi-Submersibles," In: ASME 2009 28th International Conference on Ocean. Hawaii, USA, pp. 877-887.
  18. Wang GY, Huo Y, Zhang B, Li XB, Yu ZY, 2009, "Evaluation of turbulence models for predicting the performance of an axial-flow pump," Transactions of Beijing Institute of Technology, Vol. 4, pp. 309-313.
  19. Hatano S, Kang D, Kagawa S, Nohmi M, Yokota K, 2014, "Study of cavitation instabilities in double-suction centrifugal pump," International Journal of Fluid Machinery and Systems, Vol. 7, No. 3, pp. 94-100. https://doi.org/10.5293/IJFMS.2014.7.3.094
  20. Standard A. P. I., 2010, "Centrifugal Pumps for Petroleum, Petrochemical and Natural Gas Industries," American Petroleum Institute, 11th edition.
  21. Kim JH, Choi YS, Kim JH, Cho BM, Lee KY, 2015, "Optimal design method of single channel pump impeller, single channel pump impeller and centrifugal pump designed by the method," Korean Patent No. 10-2015-0043524.
  22. Kim JH, Choi JH, Husain A, Kim KY, 2010, "Multi-objective optimization of a centrifugal compressor impeller through evolutionary algorithms," Proceedings of The Institution of Mechanical Engineers, Part A-Journal of Power and Energy, Vol. 224, No. 5, pp. 711-721. https://doi.org/10.1243/09576509JPE884
  23. Benini E, 2004, "Three-dimensional multi-objective design optimization of a transonic compressor rotor," Journal of Propulsion and Power, Vol. 20, No. 3, pp. 559-565. https://doi.org/10.2514/1.2703
  24. JMP, 2005, The Statistical Discovery Software, Version 6.0.0. SAS Institute Inc., Cary, NC, USA.
  25. Sacks J, Welch WJ, Mitchell TJ, Wynn HP, 1989, "Design and Analysis of Computer Experiments," Statistical Science, Vol. 4, No. 4, pp. 409-435. https://doi.org/10.1214/ss/1177012413
  26. Queipo NV, Haftka RT, Shyy W, Goel T, Vaidyanathan R, Tucker KP, 2005, "Surrogate-based analysis and optimization," Progress in Aerospace Sciences, Vol. 41, pp. 1-28. https://doi.org/10.1016/j.paerosci.2005.02.001
  27. Li W, Padula S, 2004, "Approximation methods for conceptual design of complex systems," 11th International Conference on Approximation Theory, pp. 1-40.
  28. Goel T, Zhao J, Thakur SS, Haftka RT, Shyy W, 2006, "Surrogate model-based strategy for cryogenic cavitation model validation and sensitivity evaluation," 42nd AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Sacramento CA, 9-12 July, AIAA-2006-5047.
  29. Orr, MJL, 1996, "Introduction to radial basis function networks," Center for Cognitive Science, Edinburgh University, Scotland, UK, http://anc.ed.ac.uk/rbf/
  30. Samad A, Kim KY, 2009, "Surrogate Based Optimization Techniques for Aerodynamic Design of Turbomachinery," International Journal of Fluid Machinery and Systems, Vol. 2, No. 2, pp. 179-188. https://doi.org/10.5293/IJFMS.2009.2.2.179
  31. MATLAB(R), 2004, The Language of Technical Computing, Release 14. The Math Works Inc.
  32. Queipo NV, Haftka RT, Shyy W, Goel T, Vaidyanathan R, Tucker PK, 2005, "Surrogate-Based Analysis and Optimization," Progress in Aerospace Sciences, Vol. 41, No. 1, pp. 1-28. https://doi.org/10.1016/j.paerosci.2005.02.001
  33. Samad A, Kim KY, Goel T, Haftka RT, Shyy W, 2008, "Multiple Surrogate Modeling for Axial Compressor Blade Shape Optimization," Journal of Propulsion and Power, Vol. 24, No. 2, pp. 302-310. https://doi.org/10.2514/1.28999

Cited by

  1. Comparative analysis of flow in a fluidic oscillator using large eddy simulation and unsteady Reynolds-averaged Navier–Stokes analysis vol.50, pp.6, 2018, https://doi.org/10.1088/1873-7005/aae946
  2. Optimized Blade Design of Counter-Rotating-Type Pump-Turbine Unit Operating in Pump and Turbine Modes vol.2018, pp.1542-3034, 2018, https://doi.org/10.1155/2018/6069780