Parameter identification of Bouc-Wen model for vacuum packed particles based on genetic algorithm

• Autorzy: Bartkowski Piotr , Zalewski Robert , Chodkiewicz Paweł

Identyfikatory

DOI

10.1016/j.acme.2018.11.002

• Języki publikacji: EN

Abstrakty

EN

This paper investigates cylindrical samples made of vacuum packed particles. Such structures are composed of granular media placed in a hermetic encapsulation where, in the final stage, a partial vacuum is generated. The main advantage of such a structure is that the underpressure value makes it possible to control the global physical properties of granular systems. Materials with various grains are analyzed in the paper. A modified Bouc-Wen hysteresis model is adopted to describe the nonlinear properties of the tested specimens. To identify the model parameters, a genetic algorithm is applied. The proposed model is found to be in good agreement with the experimental data.

Słowa kluczowe

EN

Vacuum Packed Particles; granular damper; semi-active damping; modeling; experiments

PL

amortyzator; modelowanie; eksperyment

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2019
• Tom: Vol. 19, no. 2
• Strony: 322--333
• Opis fizyczny: Bibliogr. 38 poz., fot., tab., wykr.

Twórcy

autor

Bartkowski Piotr

• Warsaw University of Technology, Faculty of Automotive and Construction Machinery Engineering, Poland

autor

Zalewski Robert

• Warsaw University of Technology, Faculty of Automotive and Construction Machinery Engineering, Poland

autor

Chodkiewicz Paweł

• Warsaw University of Technology, Faculty of Automotive and Construction Machinery Engineering, Poland

Bibliografia

• [1] R. Zalewski, P. Chodkiewicz, Gubanov model for vacuum packed particles, in: Mechatronics 2013, Springer, 2014 57–63.
• [2] P. Chodkiewicz, J. Lengiewicz, R. Zalewski, DEM modeling of Vacuum Packed Particles Damper, 2018.
• [3] Y. Parepalli, S. Reddy Pamanji, M. CHAVALI, An overview of smart materials in nanoscience and nanotechnology, Int. J. Nanosci. Nanotechnol. 3 (2014) 9–14.
• [4] J. Wang, G. Meng, Magnetorheological fluid devices: principles, characteristics and applications in mechanical engineering., Proc. Inst. Mech. Eng. L: J. Mater. Des. Appl. (2001) 165–174.
• [5] M. Luscombe, J. Williams, Comparison of a long spinal board and vacuum mattress for spinal immobilisation, Emerg. Med. J. 20 (5) (2003) 476–478.
• [6] E. Brown, N. Rodenberg, J. Amend, A. Mozeika, E. Steltz, M.R. Zakin, H. Lipson, H.M. Jaeger, Universal robotic gripper based on the jamming of granular material, Proc. Natl. Acad. Sci. 107 (44) (2010) 18809–18814.
• [7] A.J. Loeve, O.S. van de Ven, J.G. Vogel, P. Breedveld, J. Dankelman, Vacuum packed particles as flexible endoscope guides with controllable rigidity, Granular Matter 12 (6) (2010) 543–554.
• [8] A. Mozeika, E. Steltz, M.H. Jaeger, The first steps of a robot based on jamming skin enabled locomotion, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, 408–409.
• [9] A. Jiang, T. Aste, P. Dasgupta, K. Althoefer, T. Nanayakkara, Granular Jamming with Hydraulic Control, 2013.
• [10] R. Zalewski, T. Szmidt, Application of special granular structures for semi-active damping of lateral beam vibrations, Eng. Struct. 65 (2014) 13–20.
• [11] J. Bajkowski, B. Dyniewicz, C. Bajer, Corrigendum to ‘‘damping properties of a beam with vacuum-packed granular damper’’ [J. Sound Vib. 341 (2015) 74–85], J. Sound Vib. (2016) 377.
• [12] T. Szmidt, R. Zalewski, Inertially excited beam vibrations damped by vacuum packed particles, Smart Mater. Struct. 23 (10) (2014) 105026.
• [13] R. Zalewski, P. Chodkiewicz, Semi-active linear vacuum packed particles damper, J. Theor. Appl. Mech. 54 (1) (2016) 311–316.
• [14] R. Zalewski, P. Chodkiewicz, M. Shillor, Vibrations of a massspring system using a granular-material damper, Appl. Math. Modell. 40 (17-18) (2016) 8033–8047.
• [15] P. Bartkowski, R. Zalewski, A concept of smart multiaxial impact damper made of vacuum packed particles, in: MATEC Web of Conferences, page 05001. EDP Sciences, (2018) 157.
• [16] B. Spencer Jr., S. Dyke, M. Sain, J. Carlson, Phenomenological model for magnetorheological dampers, J. Eng. Mech. 123 (3) (1997) 230–238.
• [17] T. Butz, O. Von Stryk, Modelling and simulation of electroand magnetorheological fluid dampers, ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik: Applied Mathematics and Mechanics 82 (1) (2002) 3–20.
• [18] G. Yang, B.F. Spencer Jr., H.-J. Jung, J.D. Carlson, Dynamic modeling of large-scale magnetorheological damper systems for civil engineering applications, J. Eng. Mech. 130 (9) (2004) 1107–1114.
• [19] D. Gamota, F. Filisko, Dynamic mechanical studies of electrorheological materials: moderate frequencies, J. Rheol. 35 (3) (1991) 399–425.
• [20] D. Lederer, H. Igarashi, A. Kost, T. Honma, On the parameter identification and application of the Jiles-Atherton hysteresis model for numerical modeling of measured characteristics, IEEE Trans. Magnet. 35 (1999) 1211–1214.
• [21] M. Asif Zaman, C. Hansen, P.T. Neustock, L. Padhy, P.L. Hesselink, Adjoint Method for Estimating Jiles-Atherton Hysteresis Model Parameters, 120, 2016, . p. 093903.
• [22] G. Bertotti, Dynamic generalization of the scalar Preisach model of hysteresis, IEEE Trans. Magnet. 28 (1992) 2599–2601.
• [23] R. Bouc, Forced vibrations of mechanical systems with hysteresis, in: Proceedings of the Fourth Conference on Nonlinear Oscillations, Prague, (1967) 1967.
• [24] Y.-K. Wen, Method for random vibration of hysteretic systems, J. Eng. Mech. Div. 102 (2) (1976) 249–263.
• [25] M. Ye, X. Wang, Parameter estimation of the Bouc-Wen hysteresis model using particle swarm optimization, Smart Mater. Struct. 16 (2007) 2341.
• [26] M. Asif Zaman, U. Sikder, Bouc-Wen hysteresis model identification using modified firefly algorithm, J. Magnet. Magnet. Mater. 395 (2015) 229–233.
• [27] S. Kirkpatrick, D. Gelatt Jr., P.M. Vecchi, Optimization by simulated annealing, Science 220 (1983) 671–680.
• [28] M. Pyrz, M. Krzywoblocki, Crashworthiness optimization of thin-walled tubes using macro element method and evolutionary algorithm, Thin-Walled Struct. 112 (2017) 12–19.
• [29] A.S. Fraser, Simulation of genetic systems by automatic digital computers i. introduction, Aust. J. Biol. Sci. 10 (4) (1957) 484–491.
• [30] A. Fraser, D. Burnell, et al., Computer Models in Genetics, 1970.
• [31] J. Holland, D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Massachusetts, 1989.
• [32] Z. Michalewicz, Evolution strategies and other methods, in: Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1996 159–177.
• [33] J.J. Grefenstette, Genetic algorithms and machine learning., in: Proceedings of the Sixth Annual Conference on Computational Learning Theory, ACM, (1993) 3–4.
• [34] J. Shapiro, Genetic algorithms in machine learning, in: Advanced Course on Artificial Intelligence, Springer, 1999 146–168.
• [35] C. Reeves, Genetic algorithms and combinatorial optimization, Appl. Modern Heuristic Methods 111 (1995) 126.
• [36] M. Pyrz, F. Zairi, Identification of viscoplastic parameters of phenomenological constitutive equations for polymers by deterministic and evolutionary approach, Model. Simul. Mater. Sci. Eng. 15 (2) (2007) 85.
• [37] J. Zhou, B. Wang, J. Lin, L. Fu, Optimization of aluminum alloy anti-collision side beam hot stamping process using multiobjective genetic algorithm, Arch. Civil Mech. Eng. 13 (2013) 401–411.
• [38] R. Zalewski, P. Chodkiewicz, M. Pyrz, Modeling of complex properties of vacuum packed particles using evolutionary algorithms, in: New Contributions in Information Systems and Technologies, Springer, 2015 267–276.