A comparative study on parameter identification of fluid viscous dampers with different models

Fluid viscous dampers are extensively adopted as efficient and cheap energy dissipation devices in structural seismic protection. If we consider the usefulness of these passive control devices, the exact recognition of their mechanical behavior is of outstanding importance to provide a reliable support to design a very efficient protection strategy. In scientific and technical applications, many different constitutive models have been proposed and adopted till now to represent fluid viscous dampers, with different levels of complexity and accuracy. This paper focuses on parameter identification of fluid viscous dampers, comparing different existing literature models, with the aim to recognize the ability of these models to match experimental loops under different test specimens. The 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. The identification procedure furnishes the device mechanical parameters by minimizing a suitable objective function, which represents a measure of the difference between the analytical and experimental forces. To solve the 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 the frequency excitation is evaluated and discussed.

[1]  George T. Flowers,et al.  Parameter Characterisation of the Bouc/Wen Mechanical Hysteresis Model for Sandwich Composite Materials using Real Coded Genetic Algorithms , 2005 .

[2]  Bijan Samali,et al.  A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization , 2006 .

[3]  Tudor Sireteanu,et al.  Identification of an extended Bouc–Wen model with application to seismic protection through hysteretic devices , 2010 .

[4]  Zwe-Lee Gaing,et al.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004 .

[5]  Ying-Shieh Kung,et al.  A comparison of fitness functions for the identification of a piezoelectric hysteretic actuator based on the real-coded genetic algorithm , 2006 .

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  P. N. Roschke,et al.  Fuzzy modeling of a magnetorheological damper using ANFIS , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[8]  Nopdanai Ajavakom,et al.  Performance of nonlinear degrading structures: Identification, validation, and prediction , 2008 .

[9]  B Samali,et al.  Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA. , 2007, ISA transactions.

[10]  Gabriela Ciuprina,et al.  Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Mag , 2002 .

[11]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[12]  Giuseppe Carlo Marano,et al.  Identification of parameters of Maxwell and Kelvin–Voigt generalized models for fluid viscous dampers , 2015 .

[13]  Giuseppe Quaranta,et al.  Parametric Identification of Nonlinear Devices for Seismic Protection Using Soft Computing Techniques , 2013 .

[14]  Michael C. Constantinou,et al.  Experimental study of seismic response of buildings with supplemental fluid dampers , 1993 .

[15]  Anil K. Chopra,et al.  Asymmetric one‐storey elastic systems with non‐linear viscous and viscoelastic dampers: Earthquake response , 2003 .

[16]  Giuseppe Marano,et al.  Stochastic optimum design criterion of added viscous dampers for buildings seismic protection , 2007 .

[17]  Nobuyuki Matsui,et al.  GA-based evolutionary identification algorithm for unknown structured mechatronic systems , 2005, IEEE Transactions on Industrial Electronics.

[18]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[19]  Nicos Makris,et al.  Comparison of Modeling Approaches for Full-scale Nonlinear Viscous Dampers , 2008 .

[20]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[21]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[22]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[23]  D Stancioiu,et al.  MODELLING OF MAGNETORHEOLOGICAL DAMPER DYNAMIC BEHAVIOUR BY GENETIC ALGORITHMS BASED INVERSE METHOD , 2004 .

[24]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[25]  Gloria Terenzi,et al.  Dynamics of SDOF Systems with Nonlinear Viscous Damping , 1999 .

[26]  Giuseppe Marano,et al.  Stochastic optimum design criterion for linear damper devices for seismic protection of buildings , 2007 .

[27]  S. Galvani,et al.  A particle swarm optimization approach for optimum design of PID controller in linear elevator , 2010, 2010 Conference Proceedings IPEC.

[28]  T. T. Soong,et al.  Passive Energy Dissipation Systems in Structural Engineering , 1997 .

[29]  V. K. Koumousis,et al.  Identification of Bouc-Wen hysteretic systems by a hybrid evolutionary algorithm , 2008 .

[30]  Tudor Sireteanu,et al.  Model parameter identification for vehicle vibration control with magnetorheological dampers using computational intelligence methods , 2004 .

[31]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[32]  Wei-Hsin Liao,et al.  Neural network modeling and controllers for magnetorheological fluid dampers , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).