Sound Radiation Analysis of a Front Side Window Glass of DrivAer Model under Wind Excitation

He, Yikang; Shi, Zihao; Wu, Yajing; Yang, Zhigang

doi:10.1155/2018/5828725

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Although the energy is high, the impact of turbulent pressure fluctuation on the interior noise is not as good as that of acoustic pressure fluctuation. The latter has a higher frequency of influence than the former [53].…”

Section: Experimental Results and Data Pre-processingmentioning

confidence: 86%

“…Whether it is turbulent pressure excitation or acoustic pressure excitation, both of them are manifested as vibration to a certain extent, and building upon prior research [19,30,53], there exists a strong correlation between side window vibration excitation and interior wind noise within specific frequency ranges. In addition, the multiple coherence principle can be used to verify the theory [54].…”

Section: Deep Learning Theorymentioning

confidence: 90%

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Deep learning-based wind noise prediction study for automotive clay model

Huang,

Wang,

Cao

et al. 2024

Meas. Sci. Technol.

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Analyzing and mitigating wind noise in automobiles under high-speed conditions is a significant issue within the realm of Noise, Vibration, and Harshness (NVH). Due to the intricate nature of aeroacoustics generation mechanisms, current conventional methods for wind noise prediction have limitations. Hence, deep learning methods are introduced to investigate wind noise in the side window area of an automotive clay model.During aeroacoustic wind tunnel experiments, side window vibration data and noise data from the driver were collected under vehicle speed conditions of 100 km/h, 120 km/h, and 140 km/h, respectively. These data samples were obtained to be used for training and validation of the wind noise model. Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Network (LSTM) algorithms were separately employed to reveal the complex nonlinear relationship between wind noise and its influencing factors, leading to the establishment of a wind noise prediction model.Simultaneously, these two deep learning methods were compared with Backpropagation Neural Networks (BPNN), Extreme Learning Machines (ELM), and Support Vector Regression (SVR) methods. Our findings revealed that the LSTM wind noise prediction model not only exhibits higher accuracy but also demonstrates superior generalization capabilities, thereby substantiating the superiority of this method.

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Section: Experimental Results and Data Pre-processingmentioning

confidence: 86%

Section: Deep Learning Theorymentioning

confidence: 90%

Deep learning-based wind noise prediction study for automotive clay model

Huang,

Wang,

Cao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Since the study of interior noise at the low frequency range below 500 Hz has been accomplished with FEM method, 21 this work focuses only on the interior noise prediction at the middle and high frequency range with SEA approach based on the calculated convective and acoustic pressure fluctuations of vehicle exterior. In the SEA model, two subsystems were established, namely the side window and the acoustic cavity behind the glass.…”

Section: Interior Noise Calculation Of Drivaer Modelmentioning

confidence: 99%

Wind noise source characterization and transmission study through a side glass of DrivAer model based on a hybrid DES/APE method

Wen

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Based on a DrivAer model with notchback, the characteristics of convective and acoustic pressure fluctuations on the side window, as well as their contributions to interior noise were studied. Firstly, a full-size DrivAer clay model was produced with a real glass set on the front left window, and the rest parts with thick clay. In this way, the side glass becomes the exclusive transmission path for the exterior convective and acoustic pressures into acoustic cabin inside. In this study, the acoustic pressure fluctuation on the side window surface was calculated by solving the acoustic perturbation equation (APE) based on the calculation results of convective pressure fluctuation with the incompressible Detached Eddy Simulation (DES). Furthermore, with the convective and acoustic pressure fluctuations as power inputs, the interior noise was calculated with Statistical Energy Analysis (SEA). The calculated interior noise level shows good agreement with the tested results in the wind tunnel, which indirectly validates the reliability of the calculated acoustic pressures with APE method. The contributions of the convective and acoustic pressure fluctuations to the interior noise show that the acoustic pressure fluctuation takes much higher transmission efficiency than the convective one, especially at the high frequency range above the coincidence frequency of the glass, the contribution of acoustic pressure fluctuation is absolutely dominant.

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Wind noise source filtering and transmission study through a side glass of DrivAer model

Schröder

Shi

et al. 2020

Applied Acoustics

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Sound Radiation Analysis of a Front Side Window Glass of DrivAer Model under Wind Excitation

Cited by 6 publications

References 11 publications

Deep learning-based wind noise prediction study for automotive clay model

Deep learning-based wind noise prediction study for automotive clay model

Wind noise source characterization and transmission study through a side glass of DrivAer model based on a hybrid DES/APE method

Wind noise source filtering and transmission study through a side glass of DrivAer model

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