Whole-body vibration and vertical road profile displacement power spectral density

Múčka, Peter; Stein, George Juraj; Tobolka, Peter

doi:10.1080/00423114.2019.1595675

Cited by 21 publications

(10 citation statements)

References 47 publications

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“…In the signal spectrum analysis, the power spectral density is not only reflecting the signal energy change in frequency range but also reflects the vibration energy [28]. Therefore, the causes of minimum tire vibration noise reduction can be analysed from the power spectral density of the tire radial force excitation.…”

Section: Relationship Between Tire Radial Excitation Force and Noisementioning

confidence: 99%

Relationship between Tire Ground Characteristics and Vibration Noise

Zhou

Liang

et al. 2021

SV-JME

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To study the relationship between tire ground characteristic parameters and vibration noise, a radial truck tire (295/80R22.5) is selected as the research object, and a finite element model is established. The test results of the tire stiffness, vibration modal, and surface velocity response are used to verify the model. In order to find the influence of the inherent vibration characteristics of a tire on radiated noise, the contour design and belt design are selected as design schemes, and a modal analysis and vibration noise numerical simulation analysis are carried out for different tire structures. The footprints of the tire-ground are refined and divided. All the grounding parameters of each subarea are extracted, and the relationships between the geometric and mechanical parameters of the tire ground characteristics and vibration noise are investigated. Then, the effects of the skewness and stiffness of tread deformation, as well as the spectral density of tire–road excitation force power on vibration noise, are analysed. The results showed that a positive correlation exists between the vibration noise and the skewness of the tread radial deformation; however, a negative correlation is observed between the vibration noise and radial deformation stiffness. The peak values and numbers of the tire excitation force power spectral density using the minimum noise structure design are significantly smaller than those of the original tire. This study can serve as a theoretical guideline for the structural design of low-noise tires.

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Section: Relationship Between Tire Radial Excitation Force and Noisementioning

confidence: 99%

Relationship between Tire Ground Characteristics and Vibration Noise

Zhou

Liang

et al. 2021

SV-JME

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“…Similarly, the output equation can be obtained by performing inverse Laplace transform on equation (11).…”

Section: Neural Network Designmentioning

confidence: 99%

“…Gorges et al [10] presented an experimental validation of a road roughness classification method and proposed an impact detection strategy for twowheeled vehicles, including a classification of service loads, mild special events, and severe special events. Múčka et al [11] presented original new results on measured whole-body vibration (WBV) on passenger's seat surface and seat base as a function of measured road unevenness in the longitudinal direction. Mastinu et al [12] developed a new and relatively simple analytical model.…”

Section: Introductionmentioning

confidence: 99%

Research on Road Roughness Based on NARX Neural Network

Liu

Cui

2021

Mathematical Problems in Engineering

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In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

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“…Generally, PSD expresses power in a signal per unit frequency [65]. The PSD of road profile displacement is also expressed by unevenness index [66]:…”

Section: Indexes For Gravel Road Quality Assessmentmentioning

confidence: 99%

Variability of Gravel Pavement Roughness: Analysis of the Impact on Vehicle Response and Driving Comfort

Žuraulis¹,

Sivilevičius²,

Šabanovič³

et al. 2021

Preprint

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The gravel road pavement has a lower construction cost but poorer performance than the asphalt surface. It also emits dust and deforms under the impact of vehicle loads and ambient air factors. The resulting ripples and ruts are constantly deepening, increasing vehicle vibrations and fuel consumption, reducing safe driving speed and comfort. In this article, existing pavement quality evaluation indexes are analysed, and a methodology for their adaptation for roads with gravel pavement is proposed. This article reports the measured wave depth and length of the gravel pavement profile by the straightedge method of a 160 m long road section in three road exploitation stages. The measured pavement elevation was processed according to ISO 8608, and vehicle frequency response has been investigated using simulations in MATLAB/Simulink. The applied International Roughness Index (IRI) analysis showed that a speed of 30-45 km/h instead of 80 km/h provides the objective results of IRI calculation on the flexible pavement due to a decreasing velocity of vehicle's unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement has been explored, identifying specific driving safety and comfort cases. Finally, an increase in the Dynamic Load Coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrates the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers.

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Whole-body vibration and vertical road profile displacement power spectral density

Cited by 21 publications

References 47 publications

Relationship between Tire Ground Characteristics and Vibration Noise

Relationship between Tire Ground Characteristics and Vibration Noise

Research on Road Roughness Based on NARX Neural Network

Variability of Gravel Pavement Roughness: Analysis of the Impact on Vehicle Response and Driving Comfort

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