Prediction and energy contribution analysis of interior noise in a high-speed train based on modified energy finite element analysis

Zheng, Xu; Dai, Wenqiang; Qiu, Yi; Zhou, Hao

doi:10.1016/j.ymssp.2019.02.042

Cited by 28 publications

(13 citation statements)

References 36 publications

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“…where Z is the impedance at the driving point of the vibrating beam with axial force. Substituting equation (20) into equations ( 24)∼( 26), the matrix equation can be obtained as…”

Section: Solution Of Energy Density Governing Equationmentioning

confidence: 99%

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Vibrational Energy Flow Model for a High Damping Beam with Constant Axial Force

Teng

Nan

Jiang

2020

Mathematical Problems in Engineering

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The energy flow analysis (EFA) method is developed to predict the energy density of a high damping beam with constant axial force in the high-frequency range. The energy density and intensity of the beam are associated with high structural damping loss factor and axial force and introduced to derive the energy transmission equation. For high damping situation, the energy loss equation is derived by considering the relationship between potential energy and total energy. Then, the energy density governing equation is obtained. Finally, the feasibility of the EFA approach is validated by comparing the EFA results with the modal solutions for various frequencies and structural damping loss factors. The effects of structural damping loss factor and axial force on the energy density distribution are also discussed in detail.

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“…where Z is the impedance at the driving point of the vibrating beam with axial force. Substituting equation (20) into equations ( 24)∼( 26), the matrix equation can be obtained as…”

Section: Solution Of Energy Density Governing Equationmentioning

confidence: 99%

“…Park and Hong [17] and Zhihui et al [18] developed a hybrid power flow analysis method using coupling loss factor of SEA to solve the coupling problems in EFA. Kwon et al [19] and Zheng et al [20] developed an energy flow model for high-frequency vibroacoustic analysis of complex structures using EFA method.…”

Section: Introductionmentioning

confidence: 99%

Vibrational Energy Flow Model for a High Damping Beam with Constant Axial Force

Teng

Nan

Jiang

2020

Mathematical Problems in Engineering

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“…This topic was explored in [17] to show various aspects of such vibrations. Results presented in [21] described how energy consumption analysis may help in prediction of interior noise in a high speed. A wide spectrum of numerical experiments for various urban trains was presented in [7].…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network model for high-speed train vibration prediction from time series

Siłka

Wieczorek

Woźniak

2022

Neural Comput & Applic

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In this article, we want to discuss the use of deep learning model to predict potential vibrations of high-speed trains. In our research, we have tested and developed deep learning model to predict potential vibrations from time series of recorded vibrations during travel. We have tested various training models, different time steps and potential error margins to examine how well we are able to predict situation on the track. Summarizing, in our article we have used the RNN-LSTM neural network model with hyperbolic tangent in hidden layers and rectified linear unit gate at the final layer in order to predict future values from the time series data. Results of our research show the our system is able to predict vibrations with Accuracy of above 99% in series of values forward.

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“…For instance, Dai et al [10] and Zheng et al [11] proposed a statistical energy flow method to predict the full spectrum sound inside a railway vehicle but the acoustic behaviour beneath the train floor was not explained and the sound power incident on the train floor was calculated by using commercial software. Zheng et al [12] also used an energy finite element analysis method to study the noise inside a railway vehicle considering rolling noise as one of its main sources. However, again, in their approach, the noise below the vehicle was not fully explained.…”

Section: Introductionmentioning

confidence: 99%

Investigation of acoustic transmission beneath a railway vehicle by using statistical energy analysis and an equivalent source model

Thompson

Squicciarini

et al. 2021

Mechanical Systems and Signal Processing

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An approach is presented for modelling the noise propagation beneath the train floor and this is applied to rolling noise sources. It is assumed that the sound incident on the train floor is made up of a direct and a reverberant component. A combination of two numerical modelling approaches is considered to deal with these: an equivalent source model, to represent the direct component, and statistical energy analysis (SEA) for the reverberant part. In the equivalent source model, the wheel is replaced by monopole and dipole sources, which represent its radial and axial radiation. The rail vertical vibration and the sleepers are replaced by arrays of monopole sources while the rail lateral vibration is replaced by an array of lateral dipoles. The sound power of the rolling noise is obtained by using the TWINS model. In the SEA model, the region beneath the train floor is divided into several volumes and the power input to these subsystems is assumed to be due to the first reflections from the train floor and the ground. The reverberant and direct sound have very similar contributions to the total sound power incident on the train floor although this depends on how the equipment is arranged beneath the train. The modelling approach is verified by comparing the predicted sound pressure levels with laboratory measurements and with field tests.

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Prediction and energy contribution analysis of interior noise in a high-speed train based on modified energy finite element analysis

Cited by 28 publications

References 36 publications

Vibrational Energy Flow Model for a High Damping Beam with Constant Axial Force

Vibrational Energy Flow Model for a High Damping Beam with Constant Axial Force

Recurrent neural network model for high-speed train vibration prediction from time series

Investigation of acoustic transmission beneath a railway vehicle by using statistical energy analysis and an equivalent source model

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