2,918
Views
16
CrossRef citations to date
0
Altmetric
Original Articles

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

, , , , &
Pages 171-181 | Received 20 Jun 2015, Accepted 02 Dec 2015, Published online: 20 Jan 2016

References

  • Acar, E., & Rais-Rohani, M. (2009). Ensemble of metamodels with optimized weight factors. Structural and Multidisciplinary Optimization, 37(3), 279–294. doi: 10.1007/s00158-008-0230-y
  • Ansys-CFX 13.0.Ansys Inc. 2010.
  • 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.
  • 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. doi: 10.1115/1.1881697
  • 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. doi: 10.1016/j.petrol.2008.06.005
  • Collette, Y., & Siarry, P. (2003). Multiobjective optimization: Principles and case studies. New York: Springer.
  • Couckuyt, I., Turck, F. D., Dhaene, T., & Gorissen, D. (2011). Automatic surrogate model type selection during the optimization of expensive black-box problems. Proceedings of the 2011 Winter Simulation Conference, December 11–14, Phoenix, AZ, USA.
  • 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.
  • Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (1st ed.). New York: John Wiley & Sons.
  • 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. doi: 10.1016/j.compfluid.2013.04.018
  • Forrester, A. I. J., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences, 45(1–3), 50–79. doi: 10.1016/j.paerosci.2008.11.001
  • Forrester, A. I. J., Sobester, A., & Keane, A. J. (2008). Engineering design via surrogate modeling: A practical guide. Chichester: John Wiley & Sons Ltd.
  • Goel, T., Haftka, R. T., Shyy, W., & Queipo, N. V. (2007). Ensemble of surrogates. Structural and Multidisciplinary Optimization, 33(3), 199–216. doi: 10.1007/s00158-006-0051-9
  • Gorissen, D., Couckuyt, I., Laermans, E., & Dhaene, T. (2010). Multiobjective global surrogate modeling dealing with the 5-percent problem. Engineering with Computers, 26(1), 81–98. doi: 10.1007/s00366-009-0138-1
  • Gulich, J. F. (2010). Centrifugal pumps (2nd ed.). Berlin: Springer Publications.
  • 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. doi: 10.3901/CJME.2010.01.001
  • Husain, A., & Kim, K. Y. (2010). Enhanced multi-objective optimization of a microchannel heat sink through evolutionary algorithm coupled with multiple surrogate models. Applied Thermal Engineering, 30(13), 1683–1691. doi: 10.1016/j.applthermaleng.2010.03.027
  • 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 Numerical Methods in Fluids, 66(6), 742–759. doi: 10.1002/fld.2282
  • Jin, M. (2011). Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2), 61–70. doi: 10.1016/j.swevo.2011.05.001
  • 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. doi: 10.1299/kikai1938.22.55
  • Lazarkiewicz, S., & Troskolanski, A. T. (1965). Impeller pumps (1st ed.). Oxford, London: Pergamonpress Ltd.
  • Lee, Y., & Choi, D. H. (2014). Point wise ensemble of meta-models using ν nearest points cross-validation. Structural Multidisciplinary Optimization. 10.1007/s00158-014-1067-1
  • Li, W. G. (2002). The influence of number of blades on the performance of centrifugal oil pumps. WorldPumps, 427, 32–35.
  • 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. doi: 10.1007/s12206-008-0741-x
  • 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.
  • Marsis, E., Pirouzpanahand, S., & Morrison, G. (2013). CFD-based 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.
  • Myers, R. H., & Montgomery, D. C. (1995). Response surface methodology-process and product optimization using designed experiments. New York: John Wiley & Sons, Inc.
  • 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. doi: 10.1016/j.apenergy.2013.08.061
  • 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. 28.
  • Peter, J., & Onera, M. M. (2008). Comparison of surrogate models for turbomachinery design. WSEAS Transactions on Fluid Mechanics, 3(1), 10–17.
  • 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. doi: 10.1016/j.paerosci.2005.02.001
  • Robinson, T. D. (2007). Surrogate based optimization using multifidelity models with variable parameterization (PhD thesis). Massachusetts Institute of Technology.
  • 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),V01AT03A005; 7 pages. July 7–11, 2013. 10.1115/FEDSM2013-16078
  • Samad, A. (2008). Numerical optimization of turbomachinery blade using surrogate models (PhD thesis). Inha University, Republic of Korea.
  • Samad, A., & Kim, K. Y. (2008). Shape optimization of an axial compressor blade by multi-objective genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 222, 599–611.
  • 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. doi: 10.2514/1.28999
  • 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. doi: 10.1007/s00158-007-0159-6
  • 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.
  • Shi, W., Long, F., Li, Y., Leng, H., & Zou, P. (2010). Non-overload design of low specific speed submersible pump. ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting. Montreal, Quebec, Canada. 1(FEDSM-ICNMM2010–30368), 563–567. August 1–5.
  • Srinivasan, K. M. (2008). Rotodynamic pumps (1st ed.). New Delhi: New Age International (P) Ltd.
  • Taormina, R., & Chau, K. (2015). Neural network river forecasting with multi-objective fully informed particle swarm optimization. Journal of Hydroinformatics, 17(1), 99–113. doi: 10.2166/hydro.2014.116
  • Viana, F. A. C. (2011). SURROGATES toolbox user's guide version 3. Retrieved March 1, 2014, from http://sites.google.com/site/felipeacviana/surrogatestoolbox.
  • Viana, F. A. C., Simpson, T. W., Balabanov, V., & Toropov, V. (2014). Metamodeling in multidisciplinary design optimization: How far have we really come? AIAA Journal, 52(4), 670–690. doi: 10.2514/1.J052375
  • Wilcox, D. C. (1994). Turbulence modeling for CFD (2nd ed.). La Canada, California: DCW Industries, Inc.
  • 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. doi: 10.1016/j.petrol.2005.03.002
  • 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.