Frequency dependence prediction and parameter identification of rubber bushing

Li, Guang; Wu, Liguang; Zhang, Shuyu; Liu, Fang

doi:10.1038/s41598-022-04839-2

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Since this article considers the relationship between force and displacement, a nonlinear spring is used to represent the elastic element. The mechanical expression of this element can be described as [18]:…”

Section: Elastic Element Of the Rubber Bushing Modelmentioning

confidence: 99%

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Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

Chen,

Zhou

et al. 2023

Vehicles

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This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.

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Section: Elastic Element Of the Rubber Bushing Modelmentioning

confidence: 99%

“…This approach, known as "random particle updating", helps accelerate the optimization process and prevents the algorithm from getting stuck in local optima. The velocity update formula with random particle updating is given by Equation (18).…”

Section: Identification Of the Parameters Of The Viscoelastic Elementsmentioning

confidence: 99%

Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

Chen,

Zhou

et al. 2023

Vehicles

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Prediction of frequency response of sub-frame bushing and study of high-order fractional derivative viscoelastic model

Chen,

Zhou

et al. 2024

Sci Rep

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This paper presents experimental and dynamic modeling research on the rubber bushings of the rear sub-frame. The Particle Swarm Optimization algorithm was utilized to optimize a Backpropagation (BP) neural network, which was separately trained and tested across two frequency ranges: 1–40 Hz and 41–50 Hz, using wideband frequency sweep dynamic stiffness test data. The testing errors at amplitudes of 0.2 mm, 0.3 mm, and 0.5 mm were found to be 1.03%, 3.05%, and 1.96%, respectively. Subsequently, the trained neural network was employed to predict data within the frequency range of 51–70 Hz. To incorporate the predicted data into simulation software, a dynamic model of the rubber bushing was established, encompassing elastic, friction, and viscoelastic elements. Additionally, a novel model, integrating high-order fractional derivatives, was proposed based on the frequency-dependent model for the viscoelastic element. An enhanced Particle Swarm Optimization algorithm was introduced to identify the model's parameters using the predicted data. In comparison to the frequency-dependent model, the new model exhibited lower fitting errors at various amplitudes, with reductions of 3.84%, 3.61%, and 5.49%, respectively. This research establishes a solid foundation for subsequent vehicle dynamic modeling and simulation.

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Frequency dependence prediction and parameter identification of rubber bushing

Cited by 2 publications

References 21 publications

Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

Prediction of frequency response of sub-frame bushing and study of high-order fractional derivative viscoelastic model

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