Application of computational fluid dynamics and surrogate-coupled evolutionary computing to enhance centrifugal-pump performance

Engineering Applications of Computational Fluid Mechanics

Volumn 10, Issue 1, 2016, Pages 172-182

  Bellary, Sayed Ahmed Imran ^a   Adhav, Rohit ^a   Siddique, M Hamid ^a   Chon, Bo Hyun ^b   Kenyery, Frank ^c   Samad, Abdus ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

^b Inha University   (South Korea)

^c UNIVERSIDAD SIMÓN BOLÍVAR   (Venezuela)

Author keywords

Centrifugal pump impeller; Electrical submersible pump; Genetic algorithm; Multi objective optimization; Surrogate model

Indexed keywords

EID: 84961778059     PISSN: 19942060     EISSN: 1997003X     Source Type: Journal    
DOI: 10.1080/19942060.2015.1128359     Document Type: Article

References (46)

1. Acar, E., & Rais-Rohani, M. (2009). Ensemble of metamodels with optimized weight factors. Structural and Multidisciplinary Optimization, 37(3), 279-294.
2. Ansys-CFX 13.0.Ansys Inc. 2010.
3. Bellary, S. A. I., & Samad, A. (2014). An alternate approach to surrogate averaging for a centrifugal impeller shape optimization. International Journal of Computer Aided Engineering and Technology. Accepted for publication, 2014.
4. Cao, S., Peng, G., & Yu, Z. (2004). Hydrodynamic design of rotodynamicpump impeller for multiphase pumping by combined approach of inverse design and CFD analysis. Journal of Fluids Engineering, 127(2), 330-338.
5. Caridad, J., Asuaje,M., Kenyery, F., Tremante, A., & Aguillon, O. (2008). Characterization of a centrifugal pump impeller under two phase flow conditions. Journal of Petroleum Science and Engineering, 63(1-4), 18-22.
6. Collette, Y., & Siarry, P. (2003). Multiobjective optimization: Principles and case studies. NewYork: Springer.
7. Couckuyt, I., Turck, F. D., Dhaene, T., & Gorissen, D. (2011). Automatic surrogatemodel type selection during the optimization of expensive black-box problems. Proceedings of the 2011 Winter Simulation Conference, December 11-14, Phoenix, AZ, USA.
8. Cravero, C., & Macelloni, P. (2010). Design optimization of a multistage axial turbine using a response surface based strategy. Proceedings of the 2nd Int. Conference on Eng. optimization. 1-11.
9. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (1st ed.). New York: John Wiley & Sons.
10. Derakhshan, S., Pourmahdavi, M., Abdolahnejad, E., Reihani, A., & Ojaghi, A. (2013). Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm. International Journal of Computers & Fluids, 81, 145-151.
11. Forrester, A. I. J., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences, 45(1-3), 50-79.
12. Forrester, A. I. J., Sobester, A.,&Keane, A. J. (2008). Engineering design via surrogate modeling: A practical guide. Chichester: John Wiley & Sons Ltd.
13. Goel, T.,Haftka, R. T., Shyy,W.,&Queipo, N.V. (2007). Ensemble of surrogates. Structural andMultidisciplinary Optimization, 33(3), 199-216.
14. Gorissen, D., Couckuyt, I., Laermans, E., & Dhaene, T. (2010). Multiobjective global surrogatemodeling dealingwith the 5- percent problem. Engineering with Computers, 26(1), 81-98.
15. Gulich, J. F. (2010). Centrifugal pumps (2nd ed.). Berlin: Springer Publications.
16. Houlin, L., Yong,W., Shouqi, Y.,Minggao, T., & Kai,W. (2010). Effects of blade number on characteristics of centrifugal pumps. Chinese Journal of Mechanical Engineering, 23, 1-6.
17. Husain,A.,&Kim,K.Y. (2010). Enhancedmulti-objective optimization of a microchannel heat sink through evolutionary algorithm coupled with multiple surrogate models. Applied Thermal Engineering, 30 (13): 1683-1691.
18. Husain, A., Lee, K. D., & Kim, K. Y. (2011). Enhanced multi-objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods. International Journal for NumericalMethods in Fluids, 66(6), 742-759.
19. Jin, M. (2011). Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2), 61-70.
20. Kamimoto, G., & Matsuoka, Y. (1956). On the flow in the impeller of centrifugal type hydraulic machinery (The 2nd report). Transactions of the Japan Society of Mechanical Engineers, Series 3. 22(113), 55-59.
21. Lazarkiewicz, S., & Troskolanski, A. T. (1965). Impeller pumps (1st ed.). Oxford, London: Pergamonpress Ltd.
22. Lee, Y., & Choi, D. H. (2014). Point wise ensemble of metamodels using ? nearest points cross-validation. Structural Multidisciplinary Optimization. doi:10.1007/s00158-014-1067-1
23. Li, W. G. (2002). The influence of number of blades on the performance of centrifugal oil pumps. WorldPumps, 427, 32-35.
24. Luo, X., Zhang, Y., Peng, J., Xu, H., & Yu, W. (2008). Impeller inlet geometry effect on performance improvement for centrifugal pumps. Journal of Mechanical Science and Technology, 22, 1971-1976.
25. Marjavaara, B. D., Lundstrom, T. S., Goel, T., Mack, Y., & Shyy, W. (2007). Hydraulic turbine diffuser shape optimization by multiplesurrogate model approximations of Pareto fronts. Transactions of the ASME., 129, 1228-1240. September 2007.
26. Marsis, E., Pirouzpanahand, S., & Morrison, G. (2013). CFDbased design improvement for single-phase and two-phase flows inside an electrical submersible pump. ASME 2013 Fluids Engineering Division Summer Meeting, Incline village, Nevada, USA. FEDSM2013-16060, V01BT10A006; 8 pages. July 7-11.
27. Myers, R. H., & Montgomery, D. C. (1995). Response surface methodology-process and product optimization using designed experiments. NewYork: John Wiley & Sons, Inc.
28. Nguyen, A. T., Reiterand, S., & Rigo, P. A. (2014). Review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, 1043-1058.
29. Ohta, H., & Aoki, K. (1996). Effect of impeller angle on performance and internal flow of centrifugal pump for high viscosity liquids. Proceedings of the School of Engineering, Tokai University, 36(1), 159-168.
30. Peter, J.,&Onera,M.M. (2008).Comparison of surrogatemodels for turbomachinery design.WSEAS Transactions on Fluid Mechanics, 3(1), 10-17.
31. Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., & Tucker, P. K. (2005). Surrogate based analysis and optimization. Progress in Aerospace Sciences, 41(1), 1-28.
32. Robinson, T. D. (2007). Surrogate based optimization using multifidelity models with variable parameterization (PhD thesis). Massachusetts Institute of Technology.
33. Rutter, R., Sheth, K., & O'Bryan, R. (2013). Numerical flow simulation and validation of an electrical submersible pump. ASME Proceedings, Symposiumon Applicationin CFD. Incline Village, Nevada, USA. 1A(FEDSM201316078),V01 AT03A005; 7 pages. July 7-11, 2013. doi:10.1115/FEDSM 2013-16078
34. Samad, A. (2008). Numerical optimization of turbomachinery blade using surrogate models (PhD thesis). Inha University, Republic of Korea.
35. Samad, A., & Kim, K. Y. (2008). Shape optimization of an axial compressor blade bymulti-objective genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 222, 599-611.
36. Samad, A., Kim, K. Y., Goel, T., Haftka, R. T., & Shyy, W. (2008). Multiple surrogate modeling for axial compressor blade shape optimization. Journal of Propulsion and Power, 24(2), 301-310.
37. Sanchez, E., Pintos, S., & Queipo, N. V. (2008). Toward an optimal ensemble of kernel-based approximations with engineering applications. Structural and Multidisciplinary Optimization, 36, 247-261.
38. Sanda, B., & Daniela, C. V. (2012). The influence of the inlet angle over the radial impeller geometry design approach with Ansys. Journal of Engineering Studies and Research, 18(4), 32-39.
39. Shi, W., Long, F., Li, Y., Leng, H., & Zou, P. (2010). Nonoverload design of low specific speed submersible pump. ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting. Montreal, Quebec, Canada. 1(FEDSMICNMM2010- 30368), 563-567. August 1-5.
40. Srinivasan, K. M. (2008). Rotodynamic pumps (1st ed.). New Delhi: New Age International (P) Ltd.
41. Taormina, R., & Chau, K. (2015). Neural network river forecasting with multi-objective fully informed particle swarmoptimization. Journal of Hydroinformatics, 17(1), 99-113.
42. Viana, F. A. C. (2011). SURROGATES toolbox user's guide version 3. RetrievedMarch 1, 2014, fromhttp://sites.google.com/site/felipeacviana/surrogatestoolbox.
43. Viana, F. A. C., Simpson, T. W., Balabanov, V., & Toropov, V. (2014).Metamodeling inmultidisciplinary design optimization: How far have we really come? AIAA Journal, 52(4), 670-690.
44. Wilcox, D. C. (1994). Turbulence modeling for CFD (2nd ed.). La Canada, California: DCW Industries, Inc.
45. Zerpa, L. E., Queipo, N. V., Pintos, T. S., & Salager, J. L. (2005). An optimization methodology of alkaline surfactant polymer flooding processes using field scale numerical simulation and multiple surrogates. Journal of Petroleum Science and Engineering, 47, 197-208.
46. Zhang, J., & Chau, K. (2009). Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. Journal of Universal Computer Science, 15(4), 840-858.