This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Dynamic analysis prediction using Multiobjective Genetic Algorithm in rotor-bearing-coupling systems
Technical Paper
2006-01-2702
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
The study of rotative machines occupies an outstanding position in the context of machines and structures in view of significant amount of typical phenomena in the operation of those equipments. The existence of a rotative component leaning by bearings and transmitting power and torque creates a family of problems that are found in the most several machines. Therefore, in the study of the dynamic behavior of those systems, it is necessary to be determined the interaction among all the components that affect in a significant way the dynamic behavior of the system.
It is proposed, this way, to determine the dynamic behavior of Rotor-Bearing-Coupling system using a Multiobjective Genetic Algorithm coupled with Surface Response Method. The Rotor-Bearing-Coupling system has the support structure or foundation practically rigid, in way to allow the analysis of the transverse or flexional vibrations of the system. With the purpose of analyzing the dynamic behavior of the system, a simplified model was considered to represent it. However, little information exists about the dynamic behavior of the stiffness coefficients and damping parameters. For its time, approximating functions should be employed to construct polynomial surface response equations. These polynomial equations should be analyzed simultaneously and multi-objectives methods should be performed. The multi-objective genetic algorithm (NSGA) has been used in this work to solve these equations.
Recommended Content
Authors
Citation
Escobar, R. and Cavalca, K., "Dynamic analysis prediction using Multiobjective Genetic Algorithm in rotor-bearing-coupling systems," SAE Technical Paper 2006-01-2702, 2006, https://doi.org/10.4271/2006-01-2702.Also In
References
- Deb, K. Multi-Objective Optimization using Evolutionary Algorithms John Wiley and Sons Chichester 2001
- Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning Addison Wesley Longman 1989
- Mitchell, M. An Introduction to Genetic Algorithms Bradford MIT Press 1996
- Lalanne, M. Ferraris, G. Rotordynamics: Prediction in Engineering John Wiley and Sons New York 1990
- Cavalca K. L. Dedini F. G. “Experimental analysis of a tilting pad journal bearing influence in a vertical rotating system” 5 th Internetional Conference om Rotor Dynamics Darmstadt - Alemanha 7-10 September 571 582
- Myers, R. H. Montgomery, D. C. Response Surface Methodology: Process and Product Optimization Using Designed Experiments John Wiley and Sons New York 1995
- Ewins, D. J. Modal Testing Theory and Practice RSP John Wiley Letchworth 1984
- Tapia, A. T. Cavalca, K. L. Dedini, F. G. XVI Congresso Brasileiro de Engenharia Mecânica (COBEM2001) Uberlandia-Brasil Novembro 2001 44 53
- Tapia, A. T. Cavalca, K. L. Escobar, R. L Dynamic analisys of flexible couplings in rotor-bearing-coupling systems Congresso SAE Brasil 2003 São Paulo
- Holland, H. J. Adaptation in Natural and Artificial System an introductory analysis with application in biology, control and artificial intelligence Ann Arbor Michigan Press 1975
- Srinivas, N. Deb, K. Multiobjective optimization using nondominated sorting in genetic algorithms Evolutionary Computation 2 221 248 1995
- Koch, P. N. Mavris, D. Mistree, F. Multi-Level. Partioned Response Surfaces for Modeling Complex Systems 1998
- Darwin, C. The Origin of Species John Murray London
- Box, G. Hunter, S. Hunter, W. Statistics for Experiments John Wiley & Sons 1978