Calculation Process with Lattice Boltzmann and Finite Element Methods to Choose the Best Exterior Design for Wind Noise

Baudet, Guillaume; Dutrion, Cécile; Lorenzi, Remi; Gendre, Felix; Geng, Shanshan

doi:10.4271/2019-01-1471

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, they also discovered that the differences in SPL among driver’s location and passengers’ locations are minor. Baudet et al 24 developed a new numerical process upon the coupling of two models. With the first model, they computed the air flow and pressure around the vehicle using the lattice Boltzmann method.…”

Section: Wind Noisementioning

confidence: 99%

Recent progress in battery electric vehicle noise, vibration, and harshness

2021

View full text Add to dashboard Cite

As battery electric vehicle (BEV) market share grows so must our understanding of the noise, vibration, and harshness (NVH) phenomenon found inside the BEVs which makes this technological revolution possible. Similar to the conventional vehicle having encountered numerous NVH issues until today, BEV has to face many new and tough NVH issues. For example, conventional vehicles are powered by the internal combustion engine (ICE) which is the dominant noise source. The noises from other sources were generally masked by the combustion engine, thus the research focus was on the reduction of combustion engine while less attention was paid to noises from other sources. A BEV does not have ICE, automatic transmission, transfer case, fuel tank, air intake, or exhaust systems. In their place, there is more than enough space to accommodate the electric drive unit and battery pack. BEV is quieter without a combustion engine, however, the research on vehicle NVH is even more significant since the elimination of the combustion engine would expose many noise behaviors of BEV that were previously ignored but would now seem clearly audible and annoying. Researches have recently been conducted on the NVH of BEV mainly emphasis on the reduction of noise induced by powertrain, tire, wind and ancillary system and the improvement of sound quality. This review paper will focus on recent progress in BEV NVH research to advance the BEV systems in the future. It is a review for theoretical, computational, and experimental work conducted by both academia and industry in the past few years.

show abstract

Section: Wind Noisementioning

confidence: 99%

Recent progress in battery electric vehicle noise, vibration, and harshness

2021

View full text Add to dashboard Cite

show abstract

“…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. Nygren et al [9] aimed to provide a comparison of the total environmental impact from exterior noise exposure and energy demand and correlated effects of exterior noise-level and energy demand along different travel routes.…”

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

View full text Add to dashboard Cite

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.

show abstract

Calculation Process with Lattice Boltzmann and Finite Element Methods to Choose the Best Exterior Design for Wind Noise

Cited by 2 publications

References 14 publications

Recent progress in battery electric vehicle noise, vibration, and harshness

Recent progress in battery electric vehicle noise, vibration, and harshness

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

Contact Info

Product

Resources

About