A CFD Analysis Method for Prediction of Vehicle Exterior Wind Noise

Phan, Vinh Long; Tanaka, Hiroshi; Nagatani, Takaaki; Wakamatsu, Mikio; Yasuki, Tsuyoshi

doi:10.4271/2017-01-1539

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Noises caused by running airflows include: noises caused when air enters a vehicle through door windows; aerodynamic noises caused by vortexes which are generated when airflows get contact with the body, as well as vibration noises caused by friction between air and the body; vibration noises caused by convex structures on the body. From the perspective of aerodynamics, a rear view mirror outside a vehicle is a bluff body with a large structural size, which is exposed in high-speed airflows; the flow field in the wake flow region behind the rear view mirror has a complicated structure, and resistance brought by it is 2 %-5 % of air resistance of the complete vehicle; turbulent flow structures will directly affect pressure distribution in lateral window region; the shape of rear view mirrors obviously affects aerodynamic noises of the vehicle [5][6][7][8]. Therefore, it is very necessary to study flow fields and aerodynamic noises around the rear view mirror.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation and experimental test on aerodynamic noises of the bionic rear view mirror in vehicles

Wan¹,

Ma²

2017

J. vibroeng.

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At present, there are some researches focusing on optimization of aerodynamic noises on rear view mirrors, but researches on bionic noise reduction of rear view mirrors are rarely reported. Therefore, with an original rear view mirror as the basic model, the paper applied a head convex hull of a dung beetle to the original rear view mirror cover to obtain a bionic rear view mirror, then conducted numerical computation for aerodynamic noises of the bionic rear view mirror and compared the computational results with the original model. Finally, in order to verify correctness of computational results of aerodynamic noises of the rear view mirror, wind tunnel test was conducted on the rear view mirror. Experimental and numerical simulation results were highly consistent in the whole frequency band, so the wind tunnel test could be replaced by numerical simulation. Only one obvious vortex was behind the bionic rear view mirror, but two obvious vortexes with the opposite rotation directions were behind the original rear view mirror. The bionic rear view mirror did not present vortexes near the lateral window, so impacts of vortexes on noises in the vehicle could be eliminated effectively. Pressure difference in front of and behind the bionic view mirror was smaller than the pressure difference in front of and behind the original rear view mirror. Pressure resistance caused by the convex structure outside the rear view mirror was reduced, so noise reduction could be promoted. The convex structure mainly affected the aerodynamic noise in mid-high frequency regions. Compared with the original rear view mirror, noise reduction effects of the bionic rear view mirror were obvious, where the noise reduction amplitude reached 10dB. Noise source size and intensity of the bionic rear view mirror were reduced obviously.

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Section: Introductionmentioning

confidence: 99%

Numerical investigation and experimental test on aerodynamic noises of the bionic rear view mirror in vehicles

Wan¹,

Ma²

2017

J. vibroeng.

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“…This point of view has also been used in some literature to approximately estimate the correlation scale (King. 1977;Phan et al 2017;Mao et al 2023).…”

Section: Qualitative Analysis Of the Correlation Of Noise Sourcesmentioning

confidence: 99%

“…In addition to the two indexes (∂p/∂t)rms and (∂ 2 Tij/∂t 2 )rms that characterize the of dipole source and Quadrupole source intensity, it is also necessary to estimate the correlation between the noise sources (King 1977;Kim et al 2003;Phan et al 2017;Mao et al 2023). The correlation between noise sources can be characterized by correlation scales, and a larger correlation scale means that fluctuations of physical quantities in a larger area near a point are correlated to it, corresponding to higher sound radiation efficiency.…”

Section: Qualitative Analysis Of the Correlation Of Noise Sourcesmentioning

confidence: 99%

“…In theory, the correlation scale at a point on a surface (or in a fluid space) can be calculated by performing correlation calculations at the point with other points on a surface (or in a fluid space). But the excessive amount of computation makes this operation somewhat impractical (Phan et al 2017). Therefore, a approximate methods for estimating the correlation of noise sources are considered, which is to use the turbulence length scale The turbulence length scale represents the characteristic size of the local vortex.…”

Section: Qualitative Analysis Of the Correlation Of Noise Sourcesmentioning

confidence: 99%

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Numerical investigation on aerodynamic noise sources identification and noise radiation characteristics of the bogie region of high speed train

SHI,

Zhang,

2023

Preprint

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The bogie region is one of the most important aerodynamic noise sources of high-speed trains. In order to deepen the understanding of the generation mechanism and noise radiation characteristics of the aerodynamic noise in the bogie region, the unsteady flow field around the bogie region is simulated by using the DDES (Delayed Detached Eddy Simulation) model. An aerodynamic noise source identification method based on the integral solution of the FW-H (Ffowcs Williams-Hawkings) equation is performed to determine distribution of dipole and quadrupole sources in the bogie region. Combining the noise source identification results and flow field analysis, the key flow structures related to the generation of aerodynamic noise are determined and the formation mechanism of aerodynamic noise sources in the bogie region is explained from two different perspectives. The flow field data obtained by DDES simulation is also used as input for the FW-H solver to calculate far-field noise, and the source contribution, spectrum characteristics and directivity of far field noise are analyzed in detail. The results show that the jet shear layer formed at the rear edge of the cowcatcher and the front side edge of the bogie cavity are closely related to the formation of dipole and quadrupole sources in the bogie region, especially the shear vortex structures formed at the rear edge of the cowcatcher. At the speed of 350 km/h, the aerodynamic noise in the bogie region is still dominated by dipole noise, the contribution of the dipole noise generated by the bogie itself and the bogie cavity to far field noise is equally important despite the significant differences in their radiation characteristics.

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“…Sarkan et al [6] carried out a practical measurement on exterior noise of road vehicle in motion and evaluated exterior noise reductions at various levels of engine speed. Phan et al [7] used an unsteady computation fluid dynamics (CFD) simulation to predict vehicle exterior wind noise and obtain quantitative information about acoustic power of exterior wind noise in vehicle, and indicated the location of the dominant source of exterior wind noise. Baudet et al [8] presented a numerical process on the dynamic coupling of exterior parts of a vehicle to determine flow and pressure around the vehicle and optimised the vehicle shape for reducing exterior wind noise.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations

Tan

Chen²,

et al. 2020

Applied Sciences

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It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations.

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A CFD Analysis Method for Prediction of Vehicle Exterior Wind Noise

Cited by 5 publications

References 16 publications

Numerical investigation and experimental test on aerodynamic noises of the bionic rear view mirror in vehicles

Numerical investigation and experimental test on aerodynamic noises of the bionic rear view mirror in vehicles

Numerical investigation on aerodynamic noise sources identification and noise radiation characteristics of the bogie region of high speed train

A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations

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