Last modified: 2014-07-13
Abstract
This paper focuses on parameter identification of Fluid Viscous Dampers, comparing different existing literature models, with the aim to recognize ability of these models to mach experimental loops under different test specimens. Identification scheme is developed evaluating the experimental and the analytical values of the forces experienced by the device under investigation. The experimental force is recorded during the dynamic test, while the analytical one is obtained by applying a displacement time history to the candidate mechanical law.
Identification procedure furnishes device mechanical parameters by minimizing a suitable objective function, which represents a measure of difference between analytical and experimental forces. To solve optimization problem, the Particle Swarm Optimization is adopted, and the results obtained under various test conditions are shown. Some considerations about the agreement of different models with experimental data are furnished, and the sensitivity of identified parameters of analyzed models against frequency excitation is evaluated and discussed.
References
[1] Soong TT, Dargush GF (1997) Passive energy dissipation systems in structural engineering. John Wiley & Sons, New York.
[2] Marano GC,Trentadue F, Greco R (2007) Stochastic optimum design criterion for linear damper devices for seismic protection of buildings. Structural and Multidisciplinary Optimization 33:441-455
[3] Marano GC, Trentadue F, Greco R (2007) Stochastic optimum design criterion of added viscous dampers for buildings seismic protection. Structural Engineering and Mechanics 25(1): 21-37
[4] Yun HB, Tasbighoo F, Masri SF, Caffrey JP, Wolfe RW, Makris N, Black C (2008) Comparison of Modeling Approaches for Full-scale Nonlinear Viscous Dampers. Journal of Vibration and Control 14:51-76
[5] Constantinou MC and Symans MD (1993) Experimental study of seismic response of buildings with supplemental fluid dampers. Structural Design of Tall Buildings 2:93–132
[6] Schurter KC, Roschke PN (2000) Fuzzy modeling of a magnetorheological damper using ANFIS.Proceedings of the Ninth IEEE International Conference on Fuzzy Systems, San Antonio, TX, 122–127.
[7] Wang DH, Liao WH (2001) Neural network modeling and controllers for magnetorheological fluid dampers. Proceedings of the 2001 IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 1323–1326.
[8] Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications. IEEE Trans. Ind. Eletron 43:519–534
[9] Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA
[10] Iwasaki M, Miwa M, Matsui N (2005), GA-based evolutionary identification algorithm for unknown structured mechatronic systems. IEEE Trans. Ind. Electron 52: 300–305.
[11] Ha JL, Kung YS, Fung RF, Hsien SC (2006) A comparison of fitness functions for the identification of a piezoelectric hysteretic actuator based on the real-coded genetic algorithm. Sensors and Actuators A 132:643-650
[12] Kwok NM, Ha QP, Nguyen MT, Li J, Samali B (2007) Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA. ISA Transactions 46:167-179
[13] Hornig KH, Flowers GT (2005) Parameter characterization of the Bouc–Wen mechanical hysteresis model for sandwich composite materials using real coded genetic algorithms. The International Journal of Acoustics and Vibration 10(2):73–81
[14] Ajavakom N, Ng CH, Ma F (2008) Performance of nonlinear degrading structures: identification, validation, and prediction. Computer and Structure 86:652–662
[15] Giuclea M, Sireteanu T, Stancioiu D, Stammers CW (2004a) Modelling of magnetorheological damper dynamic behavior by genetic algorithms based inverse method. Proceedings of the Romanian Academy Series A 5(1):55–63
[16] Giuclea M, Sireteanu T, Stancioiu D, Stammers CW, (2004b) Model parameter identification for vehicle vibration control with magnetorheological dampers using computational intelligence methods. Proceedings of the institution of mechanical engineers. Part I: Journal of Systems and Control Engineering 218(7):569–581
[17] Kwok NM, Ha QP, Nguyen MT, Li J, Samali B (2007) Bouc–Wen model parameter identification for a MR fluid damper using computationally efficient GA. ISA Transactions, 46(2):167–179
[18] Charalampakis AE, Koumousis VK (2008) Identification of Bouc–Wen hysteretic systems by a hybrid evolutionary algorithm. Journal of Sound and Vibration 314:571–585
[19] Sireteanu T, Giuclea M, Mitu AM (2010) Identification of an extended Bouc–Wen model with application to seismic protection through hysteretic devices. Computational Mechanics 45(5):431–441
[20] Kennedy J, and Eberhart R C (1995) Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia 1942–1948
[21] Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller. AVR system, IEEE Trans. Energy Conversion 19:384–391
[22] Ciuprina G, Loan D, Munteanu I (2002) Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans. Magn. 38:1037–1040
[23] Trelea IC (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85:317–325.
[24] Kwok MN, Ha QP, Nguyen TH, Li J, Samali B (2006) A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization. Sensors and Actuators A 132: 441–451
[25] Greco R, Marano GC (2013) Identification of parameters of Maxwell and Kelvin-Voigt generalized models for fluid viscous dampers. Journal of Vibration and Control doi: 10.1177/1077546313487937.
[26] Constantinou MC and Symans MD (1993) Experimental study of seismic response of buildings with supplemental fluid dampers. Structural Design of Tall Buildings 2:93–132
[27] Constantinou MC, Symans MD, Tsopelas P, Taylor DP (1993), Fluid viscous dampers in applications of seismic energy dissipation and seismic isolation. in Proceedings of ATC 17-1 Seminar on Seismic Isolation, Passive Energy Dissipation and Active Control V. 2:581–592
[28] Lin WH, Chopra AK (2003) Asymmetric One-Storey Elastic Systems with Non-linear Viscous and Viscoelastic Dampers.Earthquake Response, Earthquake Engineering and Structural Dynamics 32:555-577
[29] Terenzi G (1999) Dynamics of SDOF Systems with Nonlinear Viscous Damping. Journal of Engineering Mechanics 125:956-963
[30] Sh, Y, Eberhart R (1998) A modified particle swarm optimizer. IEEE World Congress on Computational Intelligence, Anchorage, AK, USA, May 4-9
[31] Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation 8:240-255