Since 1963
ARTICLE
Modeling of a Bingham model of a magnetorheological damper considering stochastic uncertainties in their geometric variables
1
School of Mechanical Engineering, Hefei University of Technology, Hefei, China
Submission date: 2019-08-04
Final revision date: 2020-03-20
Acceptance date: 2020-10-24
Online publication date: 2020-11-28
Publication date: 2021-01-15
Corresponding author
Journal of Theoretical and Applied Mechanics 2021;59(1):53-65
KEYWORDS
TOPICS
solid mechanicsfluid mechanicsstability and vibrations systemsdynamics of machines, vehicles and flying structurescomputational mechanicsstructures mechanics
ABSTRACT
Stochastic uncertainty theory is used to develop a new Bingham model of magnetorheological
dampers superior to the existing model. Some input variables are defined as stochastic
variables by the stochastic factor method, and the stochastic Bingham model is developed
by the algebraic synthesis method. Curves of the damping force obtained by the stochastic
Bingham model and the Bingham model in the literature are compared with experimental
results, revealing that the curves obtained by the stochastic Bingham model are much closer
to the experimental curves. Therefore, we confirm that the stochastic Bingham model is
superior to the model from the literature.
REFERENCES (25)
1.
Ahamed R., Ferdaus M.M., LI Y.C., 2016, Advancement in energy harvesting magnetorheological fluid damper: a review, Korea-Australia Rheology Journal, 28, 355-379.
2.
Aguirre N., Ikhouane F., Rodellar J., Christenson R., 2012, Parametric identification of the Dahl model for large scale MR dampers, Structural Control and Health Monitoring, 19, 332-347.
3.
Chen J.J., 1994, Reliability of Mechanical and Structural Systems (in Chinese), 1st ed., Xi-dian University Press, Xi’an.
4.
Chen K., Yu X.P., Zheng H.M.,Wang Y.Y., Zhang G.J.,Wu R., 2018,Modeling dissipative heating of hydraulic dampers under consideration of stochastic uncertainties in their geometric parameters, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, 312.
5.
Deng Z.D., Gao F., Liu X.D., Xu G.Y., 2008, Theoretical calculation and testing of damping characteristics for magnetorheological damper on vehicle (in Chinese), Chinese Journal of Mechanical Engineering, 44, 202-207.
6.
Du X.P., Chen H., Liu Z.J., Wang C., 2016, Semi-active control of space manipulator soft contacting based on magnetorheological rotational damper, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230, 2390-2398.
7.
Gan M.G., Qiao Z., Li Y.L., 2016, Sliding mode control with perturbation estimation and hysteresis compensator based on Bouc-Wen model in tackling fast-varying sinusoidal position control of a piezoelectric actuator, Journal of Systems Science and Complexity, 29, 367-381.
8.
Hu G. L., Lu Y., Sun S.S., Li W.H., 2017, Development of a self-sensing magnetorheological damper with magnets in-line coil mechanism, Sensors and Actuators A: Physical, 255, 71-78.
9.
Jia Y.S., Zhou K.K., 2009, Rheological properties analysis and experiment of magnetorheological fluid for automobile (in Chinese), Journal of Mechanical Engineering, 45, 246-250.
10.
Liem D.T., Ahn K.K., 2016, Adaptive semi-parallel position/force-sensorless control of electrohydraulic actuator system using MR fluid damper, International Journal of Precision Engineering and Manufacturing, 17, 1451-1463.
11.
Miah M.S., Chatzi E.N., Dertimanis V.K., Weber F., 2015, Nonlinear modeling of a rotational MR damper via an enhanced Bouc-Wen model, Smart Materials and Structures, 24, 105020.
12.
Ou J.P., 2003, Structural Vibration Control-Active, Semi-Active and Intelligent Control (in Chinese), 1st ed., Science Press, Beijing.
13.
Ou J.P., Guan X.C., 1999, Experiment study of magnetorheological damper performance, Earthquake Engineering and Engineering Vibration (in Chinese), 1st ed., Science Press, Beijing.
14.
Sapiński B., Rosół M., Węgrzynowski M., 2016, Investigation of an energy harvesting MR damper in a vibration control system, Smart Materials and Structures, 25, 125017.
15.
Shames I.H., Cozzarelli F.A., 1992, Elastic and Inelastic Stress Analysis, 1st ed., Englewood Cliffs: Prentice Hall, Englewood.
16.
Spencer B.F., Dyke S.J., Sain M.K., Carlson J.D., 1997, Phenomenological model of a magnetorheological damper, Journal of Engineering Mechanics, 123, 230-238.
17.
Stanway R., Sproston J.L., Stevens N.G., 1987, Non-linear modeling of an electro-rheological vibration damper, Journal of Electrostatics, 20, 167-184.
18.
Sun S.S., Ning D.H., Yang J., Du H., Zhang S.W., Li W.H., 2016, A seat suspension with a rotary magnetorheological damper for heavy duty vehicles, Smart Materials and Structures, 25, 105032.
19.
Tang X., Du H.P., Sun S.S., D. Ning H., Xing Z.W., Li W.H., 2017, Takagi-Sugeno fuzzy control for semi-active vehicle suspension with a magnetorheological damper and experimental validation, IEEE/ASME Transactions on Mechatronics, 22, 291-300.
20.
Tao B.Q., 1997, Structure of Intelligent Material (in Chinese), 1st ed., National Defense Industry Press, Beijing.
21.
Wen Y.K., 1976, Method for random vibration of hysteretic systems, Journal of the Engineering Mechanics Division, 102, 249-263.
22.
Wereley N.M., Pang L., Kamath G.M., 1998, Idealized hysteresis modeling of electrorheological and magnetorheological damper, Journal of Intelligent Materials Systems and Structures, 9, 642-649.
23.
Yang M.G., Cai C.S., 2015, Longitudinal vibration control for a suspension bridge subjected to vehicle braking forces and earthquake excitations based on magnetorheological dampers, Journal of Vibration and Control, 22, 1-20.
24.
Zaman M.A., Sikder U., 2015, Bouc-Wen hysteresis model identification using modified firefly algorithm, Journal of Magnetism and Magnetic Materials, 395, 229-233.
25.
Zhang X.C., Zhang X., Zhao Y.X., Zhao J., Xu Z.D., 2017, Experimental and numerical studies on a composite MR damper considering magnetic saturation effect, Engineering Structures, 132, 576-585.
| eISSN: | 2543-6309 |
| ISSN: | 1429-2955 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
