Volume 19, Issue 6 (June 2019)                   Modares Mechanical Engineering 2019, 19(6): 1495-1505 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Dalilsafaei S, Montazerin N, Fazel Zarandi M. Inference System Based on Fuzzy Clustering to Display the Flow Field Changes in the Rotor Outlet of Centrifugal Turbomachinery. Modares Mechanical Engineering 2019; 19 (6) :1495-1505
URL: http://mme.modares.ac.ir/article-15-23932-en.html
1- Mechanical Engineering Department, Mechanical Engineering Faculty, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran , dalilsafaei@aut.ac.ir
2- Mechanical Engineering Department, Mechanical Engineering Faculty, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
3- Industrial Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
Abstract:   (2898 Views)
In this paper, a fuzzy clustering system is presented to display the flow field changes in the rotor outlet of centrifugal . What is important in the research done in the field of is the need for all the fields to properly understand the phenomena of flow inside the , which has complexities. For this reason, the most advanced laboratory equipment is used in this regard, which is associated with issues such as time consuming, high costs, and a large number of required tests, and doubles the importance of simulating and observing current phenomena through artificial intelligence algorithms. The present system operates on the basis of fuzzy clustering so that the spatial data (from the PIV measurement system) by the number of specific clusters to the field display in the initial time; then, by applying changes to the cluster related to the time series (from the system measurement of LDA) that contains the recorded changes of the current during the time of the data mining, the new field data are obtained at a new time step and the clustering of the data shows the variation of the flow field in the fuzzy environment. In this paper, the flow field was investigated for 6 successive steps, and the results of the system output showed the variation of the flow field from the rotor at different angles.
Full-Text [PDF 4527 kb]   (2254 Downloads)    
Article Type: Original Research | Subject: Marine Structures
Received: 2018/08/9 | Accepted: 2018/12/16 | Published: 2019/06/1

References
1. Paone N, Riethmuller ML, Vandenbraembussche RA. Application of particle image displacement velocimetry to a centrifugal pump. Unknown City: Unknown Publisher; 1998. [Link]
2. Akin O, Rockwell D. Flow structure in a radial flow pumping system using high-image-density particle image velocimetry. Journal of Fluids Engineering. 1994;116(3):538-544. [Link] [DOI:10.1115/1.2910310]
3. Sinha M, Katz J. Quantitative visualization of the flow in a centrifugal pump with diffuser vanes-I: On flow structures and turbulence. Journal of Fluids Engineering. 2000;122(1):97-107. [Link] [DOI:10.1115/1.483231]
4. Sinha M, Katz J, Meneveau Ch. Quantitative visualization of the flow in a centrifugal pump with diffuser vanes-II: Addressing passage-averaged and large-eddy simulation modeling issues in turbomachinery flows. Journal of Fluids Engineering. 2000;122(1):108-116. https://doi.org/10.1115/1.483232 [Link] [DOI:10.1115/1.483231]
5. Soranna F, Chow YC, Uzol O, Katz J. The effect of inlet guide vanes wake impingement on the flow structure and turbulence around a rotor blade. Journal of Turbomachinery. 2006;128(1):82-95. [Link] [DOI:10.1115/1.2098755]
6. Bricaud C, Richter B, Dullenkopf K, Bauer HJ. Stereo PIV measurements in an enclosed rotor-stator system with pre-swirled cooling air. Experiments in Fluids. 2005;39(2):202-212. [Link] [DOI:10.1007/s00348-005-1021-5]
7. Liu B, Yu X, Liu H, Jiang H, Yuan H, Xu Y. Application of SPIV in turbomachinery. Experiments in Fluids. 2006;40(4):621-642. [Link] [DOI:10.1007/s00348-005-0102-9]
8. Denger GR, McBride MW. Three-dimensional flow field characteristics measured in a forward-curved centrifugal blower using particle tracing velocimetry (PTV). American Society of Mechanical Engineers Fluids Engineering Division. 1990;95:49-56. [Link]
9. Cho GR, Kawahashi M, Hirahara H, Kitadume M. Application of stereoscopic particle image velocimetry to experimental analysis of flow through multiblade fan. JSME International Journal Series B Fluids and Thermal Engineering. 2005;48(1):25-33. [Link] [DOI:10.1299/jsmeb.48.25]
10. Akbarizade M, Montazerin N, Damangir E, Basirat Tabrizi H. Measurement of the flow field after the Squirrel Nest fan rotors using PIV in a fixed period phase. 11th Conference of Fluid Dynamics, 27-29 May, 2008, Tehran, Iran. Tehran: Khajeh Nasir Toosi University of Technology; 2008. [Persian] [Link]
11. Akbari G. Measurement of the turbulent field in a turbulent centrifugal turbine with SPIV method [Dissertation]. Tehran: Amirkabir University of Technology; 2013. [Link]
12. Goguen JA. LA Zadeh. Fuzzy sets. Information and control, vol. 8 (1965), pp. 338-353. LA Zadeh. Similarity relations and fuzzy orderings. Information sciences, Vol. 3 (1971), pp. 177-200. The Journal of Symbolic Logic. 1973;38(4):656-657. [Link] [DOI:10.2307/2272014]
13. Panigrahi PK, Dwivedi M, Khandelwal V, Sen M. Prediction of turbulence statistics behind a square cylinder using neural networks and fuzzy logic. Journal of Fluids Engineering. 2003;125(2):385-387. [Link] [DOI:10.1115/1.1537251]
14. Tseng YH, Durbin P, Tzeng GH. Using a fuzzy piecewise regression analysis to predict the nonlinear time-series of turbulent flows with automatic change-point detection. Flow Turbulence and Combustion. 2001;67(2):81-106. [Link] [DOI:10.1023/A:1014077330409]
15. Liang Z, Shan Sh, Liu X, Wen Y. Fuzzy prediction of AWJ turbulence characteristics by using typical multi-phase flow models. Engineering Applications of Computational Fluid Mechanics. 2017;11(1):225-257. [Link] [DOI:10.1080/19942060.2016.1277556]
16. Vernet A, Kopp GA. Classification of turbulent flow patterns with fuzzy clustering. Engineering Applications of Artificial Intelligence. 2002;15(3-4):315-326. [Link] [DOI:10.1016/S0952-1976(02)00037-4]
17. Ruspini EH. A new approach to clustering. Information and Control. 1969;15(1):22-32. [Link] [DOI:10.1016/S0019-9958(69)90591-9]
18. Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences. 1984;10(2-3):191-203. [Link] [DOI:10.1016/0098-3004(84)90020-7]
19. Pourmohammadi S, Maleki A. An automatic approach to continuous stress assessment during driving based on fuzzy c-means clustering. The Modares Journal of Electrical Engineering. 2013;13(1):9-17. [Link]
20. Ghosh S, Mitra S, Dattagupta R. Fuzzy clustering with biological knowledge for gene selection. Applied Soft Computing. 2014;16:102-111. [Link] [DOI:10.1016/j.asoc.2013.11.007]
21. Alesheikh AA, Aslani M, Kalantari SM. Extracting optimal fuzzy knowledge base for integration weighting and integrating spatial information of the geospatial information systems. The Journal of Spatial Planning. 2013;17(1):21-42. [Persian] [Link]
22. Eslamloueyan R. Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process. Applied Soft Computing. 2011;11(1):1407-1415. [Link] [DOI:10.1016/j.asoc.2010.04.012]
23. Zhao F, Liu H, Fan J. A multiobjective spatial fuzzy clustering algorithm for image segmentation. Applied Soft Computing. 2015;30:48-57. [Link] [DOI:10.1016/j.asoc.2015.01.039]
24. Sowmya B, Sheela Rani B. Colour image segmentation using fuzzy clustering techniques and competitive neural network. Applied Soft Computing. 2011;11(3):3170-3178. [Link] [DOI:10.1016/j.asoc.2010.12.019]
25. Yang Z, Chung FL, Shitong W. Robust fuzzy clustering-based image segmentation. Applied Soft Computing. 2009;9(1):80-84. [Link] [DOI:10.1016/j.asoc.2008.03.009]
26. Ghorbanpour A, tallai G, panahi M. Clustering customers of Refah Bank branches using combination of genetic algorithm and c-means in fuzzy environment. Organizational Resources Management Researchs. 2015;5(3):153-168. [Persian] [Link]
27. Askari S, Montazerin N. A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering. Expert Systems with Applications. 2015;42(4):2121-2135. [Link] [DOI:10.1016/j.eswa.2014.09.036]
28. Askari S, Montazerin N, Fazel Zarandi MH. A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables. Applied Soft Computing. 2015;35:151-160. [Link] [DOI:10.1016/j.asoc.2015.06.028]
29. Duru O, Bulut E. A non-linear clustering method for fuzzy time series: Histogram damping partition under the optimized cluster paradox. Applied Soft Computing. 2014;24:742-748. [Link] [DOI:10.1016/j.asoc.2014.08.038]
30. Raj D, Swim WB. Measurements of the mean flow velocity and velocity fluctuations at the exit of an FC centrifugal fan rotor. Journal of Engineering for Power. 1981;103(2):393-399. [Link] [DOI:10.1115/1.3230733]
31. Raffel M, Willert CE, Scarano F, Kähler CJ, Wereley ST, Kompenhans J. Particle image velocimetry: A practical guide. 3rd Edition. Cham: Springer International Publishing; 2018. [Link] [DOI:10.1007/978-3-319-68852-7]
32. Tropea C. Laser Doppler anemometry: Recent developments and future challenges. Measurement Science and Technology. 1995;6(6):605-619. [Link] [DOI:10.1088/0957-0233/6/6/001]
33. Groll L, Jakel J. A new convergence proof of fuzzy c-means. IEEE Transactions on Fuzzy Systems. 2005;13(5):717-720. [Link] [DOI:10.1109/TFUZZ.2005.856560]
34. Koorehpazan Dezfuli A. Principles of fuzzy set theory and its applications in the modeling of water engineering problems. 2nd Edition. Tehran: Amir Kabir Jahad Daneshgani Department of Education; 2008. [Persian] [Link]
35. Fukuyama Y. A new method of choosing the number of clusters for the fuzzy c-mean method. In: Proceeding of 5th Fuzzy System Symposium. Unknown City: Unknown Publisher; 1989. pp. 247-250. [Link]
36. Xie XL, Beni G. A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991;13(8):841-847. [Link] [DOI:10.1109/34.85677]
37. Kwon SH. Cluster validity index for fuzzy clustering. Electronics Letters. 1998;34(22):2176-2177. [Link] [DOI:10.1049/el:19981523]
38. Askari S, Montazerin N, Fazeli Zarandi MH. Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data. Applied Soft Computing. 2017;53:262-283. [Link] [DOI:10.1016/j.asoc.2016.12.049]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.