In the present study, the effective parameters of centrifugal pumps are investigated using the EFAST Sensitivity Analysis (SA) method. The SA is performed using GMDH type artificial neural networks (ANN) which are based on validated numerical data of flow field in centrifugal pumps. There are four design variables namely: leading edge angle of blades on hub section (β_{1 Hub}), leading edge angle of blades on shroud section (β_{1 Shroud}), trailing edge angle of blades (β_{2}), and the stagger angle of blades on mid span (γ _{mid}) and there are two objective functions namely: efficiency (h) and the required NPSH of impeller. The results show that among design variables, β_{2 }has the highest effect on variations of h (46%) and NPSH (45%). Except β_{2}, β_{1 Hub} and γ_{mid} has the highest effect on NPSH (33%) and h (28%) respectively. The effects of all of the design variables on objective functions are shown in the results.
Demeulenaere, A., Purwanto, A., Ligout, A., Hirsch, C., Dijkers, R., and Visser, F. Design and optimization of an industrial pump: Application of genetic algorithm and neural network", Proceedings of Insert Conference Abbreviation, ASME Fluid Engineering Summer Conference, Houston, Texas (2005). 2. Nariman-zadeh, N., Amanifard, N., Hajiloo, A., Ghalandari, P., and Hoseinpoor, B. Multi-objective pareto optimization of centrifugal pumps using genetic algorithms", Proceedings of 11th WSEAS International Conference on Computers, Crete Island, Greece (2007). 3. Sa_khani, H., Khalkhali, A., and Farajpoor, M. Pareto based multi-objective optimization of centrifugal pumps using CFD, neural networks and genetic algorithms", Engineering Applications of Computational Fluid Mechanics, 5, pp. 37-48 (2011). 4. Korakianitis, T., Rezaienia, M., Gordon, P., Rahideh, A., Rothman, T., and Mozafari, S. Optimization of centrifugal pump characteristic dimensions for meH. Sa_khani/Scientia Iranica, Transactions B: Mechanical Engineering 26 (2019) 421{427 427 chanical circulatory support devices", ASAIO Journal, 62(5), pp. 545-551 (2016). 5. Wang, C., Shi, W., Wang, X., Jiang, X., Yang, Y., Li, W., and Zhou, L. Optimal design of multistage centrifugal pump based on the combined energy loss model and computational uid dynamics", Applied Energy, 187, pp. 10-26 (2017). 6. Wang, W., Shouqi, Y., and Ji, P. Optimization of the di_user in a centrifugal pump by combining response surface method with multi-island genetic algorithm", Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 231(2), pp. 13-24 (2017). 7. Zhao, A., Lai, Z., Wu, P., Cao, L., and Wu, D. Multiobjective optimization of a low speci_c speed centrifugal pump using an evolutionary algorithm", Engineering Optimization, 48(7), pp. 1251-1274 (2016). 8. Yun, X., Lei, T., and Shuliang, C. Multiparameter and multiobjective optimization design of centrifugal pump based on orthogonal method", Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 231(14), pp. 19-28 (2017). 9. Sa_khani, H. Modeling and multi-objective Pareto optimization of new cyclone separators using CFD, ANNs and NSGA II algorithm", Advanced Powder Technology, 27(5), pp. 2277-2284 (2016). 10. Sa_khani, H. and Dolatabadi, H. Multi-objective optimization of cooling of a stack of vertical minichannels and conventional channels subjected to natural convection", Applied Thermal Engineering, 96, pp. 144-150 (2016). 11. Damavandi, M.D., Forouzanmehr, M., and Sa_khani, H. Modeling and pareto based multi-objective optimization of wavy _n-and-elliptical tube heat exchangers using CFD and NSGA-II algorithm", Applied Thermal Engineering, 111, pp. 325-339 (2017). 12. Sada_, M.H., Hosseini, R., Sa_khani, H., Bagheri, A., and Mahmoodabadi, M.J. Multi-objective optimization of solar thermal energy storage using hybrid of particle swarm optimization, multiple crossover and mutation operator", International Journal of Engineering, 24(3), pp. 366-376 (2011). 13. Sa_khani, H. and Eiamsa-ard, S. Pareto based multiobjective optimization of turbulent heat transfer ow in helically corrugated tubes", Applied Thermal Engineering, 95, pp. 275-280 (2016). 14. Saltelli, A. and Sobol, M. About the use of rank transformation in sensitivity analysis of model output", Reliability Engineering & System Safety, 50, pp. 225-239 (1995). 15. Saltelli, A., Chan, K., and Scott, E., Sensitivity Analysis Wiley Series in Probability and Statistics, Willey, New York (2000). 16. Korayem, M., Rastegar, Z., and Taheri, M. Sensitivity analysis of nano-contact mechanics models in manipulation of biological cell", Nanoscience and Nanotechnology, 2, pp. 49-56 (2012). 17. Tong, C. Self-validated variance-based methods for sensitivity analysis of model outputs", Reliability Engineering & System Safety, 95, pp. 301-309 (2010). 18. Nossent, J., Elsen, P., and Bauwens, W. Sobol' sensitivity analysis of a complex environmental model", Environmental Modelling & Software, 26, pp. 1515- 1525 (2011). 19. Sobol, I.M. Sensitivity estimates for nonlinear mathematical models", Mathematical Modeling and Computational Experiments, 14, pp. 407-414 (1993). 20. Cukier, R., Levine, H., and Shuler, K. Nonlinear sensitivity analysis of multiparameter model systems", Journal of Computational Physics, 26, pp. 1-42 (1978). 21. Saltelli, A., Tarantola, S., and Chan, K.-S. A quantitative model-independent method for global sensitivity analysis of model output", Technometrics, 41, pp. 39- 56 (1999). 22. Homma, T. and Saltelli, A. Importance measures in global sensitivity analysis of nonlinear models", Reliability Engineering & System Safety, 52, pp. 1-17 (1996).
Safikhani, H. (2019). Sensitivity analysis of the effective centrifugal pump parameters using the EFAST method. Scientia Iranica, 26(1), 421-427. doi: 10.24200/sci.2017.20008
MLA
H. Safikhani. "Sensitivity analysis of the effective centrifugal pump parameters using the EFAST method". Scientia Iranica, 26, 1, 2019, 421-427. doi: 10.24200/sci.2017.20008
HARVARD
Safikhani, H. (2019). 'Sensitivity analysis of the effective centrifugal pump parameters using the EFAST method', Scientia Iranica, 26(1), pp. 421-427. doi: 10.24200/sci.2017.20008
VANCOUVER
Safikhani, H. Sensitivity analysis of the effective centrifugal pump parameters using the EFAST method. Scientia Iranica, 2019; 26(1): 421-427. doi: 10.24200/sci.2017.20008