A novel reliable parametric model for predicting the nonlinear hysteresis phenomenon of composite magnetorheological fluid
Magnetorheological fluid (MRF), as a novel intelligent composite material, possesses unique controllable properties in the presence of a magnetic field, thereby opening up new possibilities for its engineering applications. This study proposes a novel parametric model to predict the nonlinear hyster...
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| Published in | Smart materials and structures Vol. 34; no. 3; pp. 35060 - 35078 |
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| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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IOP Publishing
01.03.2025
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| Abstract | Magnetorheological fluid (MRF), as a novel intelligent composite material, possesses unique controllable properties in the presence of a magnetic field, thereby opening up new possibilities for its engineering applications. This study proposes a novel parametric model to predict the nonlinear hysteresis behavior of MRF using micron-scale carbonyl iron particles. Experiments with large-amplitude shear tests (10% strain amplitude, 0.1 Hz and 1 Hz frequencies) were conducted at five current levels (0 A, 0.5 A, 1 A, 1.5 A, and 2 A) to identify model parameters via a genetic optimization algorithm. The proposed model, with fewer parameters and no differential operators, outperforms existing models (e.g. Bouc–Wen and hyperbolic tangent models) in capturing MRF’s nonlinear behavior. This research provides a robust theoretical framework for predicting the nonlinear hysteresis in automotive dampers and semi-active suspension control. |
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| AbstractList | Magnetorheological fluid (MRF), as a novel intelligent composite material, possesses unique controllable properties in the presence of a magnetic field, thereby opening up new possibilities for its engineering applications. This study proposes a novel parametric model to predict the nonlinear hysteresis behavior of MRF using micron-scale carbonyl iron particles. Experiments with large-amplitude shear tests (10% strain amplitude, 0.1 Hz and 1 Hz frequencies) were conducted at five current levels (0 A, 0.5 A, 1 A, 1.5 A, and 2 A) to identify model parameters via a genetic optimization algorithm. The proposed model, with fewer parameters and no differential operators, outperforms existing models (e.g. Bouc–Wen and hyperbolic tangent models) in capturing MRF’s nonlinear behavior. This research provides a robust theoretical framework for predicting the nonlinear hysteresis in automotive dampers and semi-active suspension control. |
| Author | Wang, Jiong Chen, Guinan Zhang, Guang Ouyang, Qing Yu, Yang Zhang, Zheng Chen, Junyu Luo, Jiahao Qiu, Ye Sun, Min Shen, Teng Chen, Bo Liu, Qianwei |
| Author_xml | – sequence: 1 givenname: Guang surname: Zhang fullname: Zhang, Guang organization: Zhejiang Zuansheng Technology Co. , Ltd, Lishui 323000, People’s Republic of China – sequence: 2 givenname: Jiahao surname: Luo fullname: Luo, Jiahao organization: Zhejiang University of Technology Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Hangzhou 310014, People’s Republic of China – sequence: 3 givenname: Min orcidid: 0000-0001-7750-9751 surname: Sun fullname: Sun, Min organization: XGM Corporation Limited , Taizhou 317100, People’s Republic of China – sequence: 4 givenname: Yang orcidid: 0009-0001-9795-5430 surname: Yu fullname: Yu, Yang organization: University of New South Wales School of Civil and Environmental Engineering, Sydney 2052, Australia – sequence: 5 givenname: Junyu orcidid: 0000-0003-2309-2190 surname: Chen fullname: Chen, Junyu organization: Nanjing University of Science and Technology School of Mechanical Engineering, Nanjing 210094, People’s Republic of China – sequence: 6 givenname: Jiong surname: Wang fullname: Wang, Jiong organization: Nanjing University of Science and Technology School of Mechanical Engineering, Nanjing 210094, People’s Republic of China – sequence: 7 givenname: Qing orcidid: 0000-0002-2028-3365 surname: Ouyang fullname: Ouyang, Qing organization: Jiaxing University School of Information Science and Engineering, Jiaxing, People’s Republic of China – sequence: 8 givenname: Ye surname: Qiu fullname: Qiu, Ye organization: Zhejiang University of Technology College of Mechanical Engineering, Hangzhou 310014, People’s Republic of China – sequence: 9 givenname: Guinan surname: Chen fullname: Chen, Guinan organization: Zhejiang University of Technology College of Mechanical Engineering, Hangzhou 310014, People’s Republic of China – sequence: 10 givenname: Qianwei surname: Liu fullname: Liu, Qianwei organization: Zhejiang University of Technology College of Mechanical Engineering, Hangzhou 310014, People’s Republic of China – sequence: 11 givenname: Bo surname: Chen fullname: Chen, Bo organization: Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou 310027, People’s Republic of China – sequence: 12 givenname: Teng orcidid: 0000-0003-0423-8836 surname: Shen fullname: Shen, Teng organization: Guangzhou University School of Mechanical and Electrical Engineering, Guangzhou, People’s Republic of China – sequence: 13 givenname: Zheng orcidid: 0000-0002-1398-7727 surname: Zhang fullname: Zhang, Zheng organization: XGM Corporation Limited , Taizhou 317100, People’s Republic of China |
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| Cites_doi | 10.1016/j.engstruct.2013.03.006 10.1088/1361-665X/aacd32 10.1177/1045389X13502867 10.1016/j.matdes.2020.109074 10.1088/1361-665X/acb577 10.1061/(ASCE)0733-9399(1997)123:3(230) 10.1177/1077546320959290 10.1007/s11071-016-2684-6 10.2514/3.61511 10.15632/jtam-pl/128811 10.1109/TCYB.2013.2250955 10.1088/1361-665X/ad9676 10.1088/1361-665X/ac6193 10.1088/1361-665X/ac701a 10.1039/c0sm01221a 10.1016/j.compstruct.2023.117082 10.1088/1361-665X/abadd2 10.1016/j.molliq.2021.117377 10.1177/1045389X12436735 10.1016/j.compstruct.2021.114119 10.1016/j.apm.2023.09.027 10.1016/j.compstruct.2021.114566 10.1021/acsaelm.1c00070 10.3390/act10100257 10.1088/1361-665X/ad622b 10.1177/1045389X15577649 10.1063/1.3503969 10.1016/j.sna.2006.03.015 10.1016/j.apm.2013.12.006 |
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| Snippet | Magnetorheological fluid (MRF), as a novel intelligent composite material, possesses unique controllable properties in the presence of a magnetic field,... |
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| SubjectTerms | genetic algorithm magnetorheological fluid nonlinear hysteresis phenomenon parameter identification parametric model |
| Title | A novel reliable parametric model for predicting the nonlinear hysteresis phenomenon of composite magnetorheological fluid |
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