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

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Abstract

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.

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References

  1. Soong T.T., Dargush G.F.: Passive Energy Dissipation Systems in Structural Engineering. Wiley, New York (1997)

    Google Scholar 

  2. Marano G.C., Trentadue F., Greco R.: Stochastic optimum design criterion for linear damper devices for seismic protection of buildings. Struct. Multidiscip. Optim. 33, 441–455 (2007)

    Article  Google Scholar 

  3. Marano G.C., Trentadue F., Greco R.: Stochastic optimum design criterion of added viscous dampers for buildings seismic protection. Struct. Eng. Mech. 25(1), 21–37 (2007)

    Article  Google Scholar 

  4. Yun H.B., Tasbighoo F., Masri S.F., Caffrey J.P., Wolfe R.W., Makris N., Black C.: Comparison of modeling approaches for full-scale nonlinear viscous dampers. J. Vib. Control 14, 51–76 (2008)

    Article  MATH  Google Scholar 

  5. Constantinou M.C., Symans M.D.: Experimental study of seismic response of buildings with supplemental fluid dampers. Struct. Des. Tall Build. 2, 93–132 (1993)

    Article  Google Scholar 

  6. Schurter, K.C., Roschke, P.N.: Fuzzy modeling of a magnetorheological damper using ANFIS. In: Proceedings of the Ninth IEEE International Conference on Fuzzy Systems. San Antonio, TX, pp. 122–127 (2000)

  7. Wang, D.H., Liao, W.H.: Neural network modeling and controllers for magnetorheological fluid dampers. In: Proceedings of the 2001 IEEE International Conference on Fuzzy Systems. Melbourne, Australia, pp. 1323–1326 (2001)

  8. Man K.F., Tang K.S., Kwong S.: Genetic algorithms: concepts and applications. IEEE Trans. Ind. Electron. 43, 519–534 (1996)

    Article  Google Scholar 

  9. Goldberg D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  10. Iwasaki M., Miwa M., Matsui N.: GA-based evolutionary identification algorithm for unknown structured mechatronic systems. IEEE Trans. Ind. Electron. 52, 300–305 (2005)

    Article  Google Scholar 

  11. Ha J.L., Kung Y.S., Fung R.F., Hsien S.C.: A comparison of fitness functions for the identification of a piezoelectric hysteretic actuator based on the real-coded genetic algorithm. Sens. Actuators A 132, 643–650 (2006)

    Article  Google Scholar 

  12. Kwok N.M., Ha Q.P., Nguyen M.T., Li J., Samali B.: Bouc–Wen model parameter identification for a MR fluid damper using computationally efficient GA. ISA Trans. 46, 167–179 (2007)

    Article  Google Scholar 

  13. Hornig K.H., Flowers G.T.: Parameter characterization of the Bouc–Wen mechanical hysteresis model for sandwich composite materials using real coded genetic algorithms. Int. J. Acoust. Vib. 10(2), 73–81 (2005)

    Google Scholar 

  14. Ajavakom N., Ng C.H., Ma F.: Performance of nonlinear degrading structures: identification, validation, and prediction. Comput. Struct. 86, 652–662 (2008)

    Article  Google Scholar 

  15. Giuclea M., Sireteanu T., Stancioiu D., Stammers C.W.: Modelling of magnetorheological damper dynamic behavior by genetic algorithms based inverse method. Proc. Rom. Acad. Ser. A 5(1), 55–63 (2004a)

    Google Scholar 

  16. Giuclea M., Sireteanu T., Stancioiu D., Stammers C.W.: Model parameter identification for vehicle vibration control with magnetorheological dampers using computational intelligence methods. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 218(7), 569–581 (2004b)

    Article  Google Scholar 

  17. Kwok N.M., Ha Q.P., Nguyen M.T., Li J., Samali B.: Bouc–Wen model parameter identification for a MR fluid damper using computationally efficient GA. ISA Trans. 46(2), 167–179 (2007)

    Article  Google Scholar 

  18. Charalampakis A.E., Koumousis V.K.: Identification of Bouc–Wen hysteretic systems by a hybrid evolutionary algorithm. J. Sound Vib. 314, 571–585 (2008)

    Article  Google Scholar 

  19. Sireteanu T., Giuclea M., Mitu A.M.: Identification of an extended Bouc–Wen model with application to seismic protection through hysteretic devices. Comput. Mech. 45(5), 431–441 (2010)

    Article  MATH  Google Scholar 

  20. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. Perth, Australia, pp. 1942–1948 (1995)

  21. Gaing Z.L.: A particle swarm optimization approach for optimum design of PID controller. AVR system. IEEE Trans. Energy Convers. 19, 384–391 (2004)

    Article  Google Scholar 

  22. Ciuprina G., Loan D., Munteanu I.: Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans. Magn. 38, 1037–1040 (2002)

    Article  Google Scholar 

  23. Trelea I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. IEEE Trans. Magn. 85, 317–325 (2003)

    MATH  MathSciNet  Google Scholar 

  24. Kwok M.N., Ha Q.P., Nguyen T.H., Li J., Samali B.: A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization. Sens. Actuators A 132, 441–451 (2006)

    Article  Google Scholar 

  25. Greco, R., Marano, G.C.: Identification of parameters of Maxwell and Kelvin–Voigt generalized models for fluid viscous dampers. J. Vib. Control (2013). doi:10.1177/1077546313487937

  26. Marano G.C., Greco R., Quaranta G., Fiore A., Avakian J., Cascella D.: Parametric identification of nonlinear devices for seismic protection using soft computing techniques. Adv. Mater. Res. 639–640((1)), 118–129 (2013)

    Article  Google Scholar 

  27. Constantinou M.C., Symans M.D.: Experimental study of seismic response of buildings with supplemental fluid dampers. Struct. Des. Tall Build. 2, 93–132 (1993)

    Article  Google Scholar 

  28. Constantinou, M.C., Symans, M.D., Tsopelas, P., Taylor, D.P.: 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, vol. 2, pp. 58–592 (1993)

  29. Lin W.H., Chopra A.K.: Asymmetric one-storey elastic systems with non-linear viscous and viscoelastic dampers. Earthq. Response Earthq. Eng. Struct. Dyn. 32, 555–577 (2003)

    Article  Google Scholar 

  30. Terenzi G.: Dynamics of SDOF systems with nonlinear viscous damping. J. Eng. Mech. 125, 956–963 (1999)

    Article  Google Scholar 

  31. Sh, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, Anchorage, AK, USA (1998)

  32. Ratnaweera A., Halgamuge S.K., Watson H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8, 240–255 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. DICATECH, Department of Civil Engineering, Environmental, Territory, Building and Chemical, Technical University of Bari, via Orabona 4, 70125, Bari, Italy

    Rita Greco

  2. SISMLAB srl, via Del Tratturello Tarantino, 6, Taranto, Italy

    Jennifer Avakian

  3. DICAR, Department of Civil Engineering and Architecture, Technical University of Bari, via Orabona 4, 70125, Bari, Italy

    Giuseppe Carlo Marano

Authors
  1. Rita Greco
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  2. Jennifer Avakian
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  3. Giuseppe Carlo Marano
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Correspondence to Rita Greco.

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Greco, R., Avakian, J. & Marano, G.C. A comparative study on parameter identification of fluid viscous dampers with different models. Arch Appl Mech 84, 1117–1134 (2014). https://doi.org/10.1007/s00419-014-0869-3

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  • DOI: https://doi.org/10.1007/s00419-014-0869-3

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