Coupling CFD with Vibroacoustic FE Models for Vehicle Interior Low-Frequency Wind Noise Prediction

Glandier, Christian; Eiselt, Mark; Prill, Oskar; Bauer, Eric R.

doi:10.4271/2015-01-2330

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…It involves subdividing the vehicle's structure into numerous small elements and numerically simulating the propagation of sound waves within each element to predict the overall noise level of the vehicle. The advantage of this method lies in its ability to consider both internal and external noise propagation as well as the influence of the vehicle's structure [26]. Some studies have proposed coupling time-domain CFD calculations with finite element models of vehicle vibration to predict low-frequency wind noise below 500 Hz.…”

Section: Numerical Simulation Methods For Wind Noise Predictionmentioning

confidence: 99%

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: Numerical Simulation Methods For Wind Noise Predictionmentioning

confidence: 99%

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

Huang,

Wang,

Cao

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Techniques to assess the underfloor contribution experimentally in the wind tunnel generally involve blocking the underfloor region with a full 'skirt' to isolate this from the flow, typically used when validating computation approaches. 67,69,70 Other noise sources. A range of other aerodynamic noise sources are also present on a vehicle.…”

Section: Vehicle Formmentioning

confidence: 99%

Automotive aeroacoustics: An overview

Oettle

Sims-Williams

2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractVehicle aeroacoustic performance has a strong influence on customer perception and also has importance for safety and comfort. Wind noise performance was once differentiated by the quality of sealing. Today, achieving competitive wind noise performance also depends on minimising aeroacoustic noise sources generated by the vehicle form, and on attenuation in the noise pathway from sources on the exterior to the vehicle interior. Attenuation in the noise pathway from sources on the exterior to the vehicle interior, especially through glazed surfaces, will continue to play an important role in controlling cabin noise, with a particular emphasis on achieving attenuation efficiently in terms of component mass. The human brain is not only sensitive towards the level of steady broadband noise, but distinctive features such as tonality or modulation draw the attention of the vehicle occupant and impact negatively on perception. Complex indices are often required to define good wind noise performance. This includes the consideration of multiple frequency bands and effects of the range of yaw angles experienced on-road. A key to achieving future vehicle refinement is bringing together an understanding of unsteady onset flow conditions, their impact on cabin sound pressure level and modulation and in turn the impact of noise level and modulation on psychoacoustic perception.

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“…By attaching a skirt around the front and sides of the vehicle to seal the ground clearance, the influence of the underbody flow-induced can be determined, as was carried out in the works. 5,6 The experimental results revealed that the contribution of the underbody flow-induced noise to the cabin could be up to 12 dB(A) lower than the sound pressure level (SPL) at driver's ear without a blocking skirt. 6 However, due to the high cost of wind tunnel experiments and pressures on the product development cycle, the method of digital simulation can be an alternative in the early stages of the design process.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation on the contribution of underbody flow-induced noise on vehicle interior noise

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Aerodynamic noise transmitted through greenhouse panels and sealing often dominates the higher frequencies of the interior noise level, whereas the underbody area contributes mainly to low and middle frequencies. A method that unsteady Computational Fluid Dynamics (CFD) for exterior airflow combined with Finite Element Method (FEM) for interior acoustic response was used. To validate the accuracy of this method, the interior wind noise of a simplified vehicle model proposed by Hyundai was computed. The comparison between the computational and experimental result showed that this method had enough accuracy to compute the interior wind noise induced by the exterior flow field. Then, the same method was used to compute the wind noise transmitted through the underbody of a passenger car. The characteristic of the noise source and noise inside the cabin was revealed, and the contribution of underbody flow-induced noise to the interior noise was also investigated. Finally, the influence of the underbody panels thicknesses on the interior wind noise was evaluated.

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Coupling CFD with Vibroacoustic FE Models for Vehicle Interior Low-Frequency Wind Noise Prediction

Cited by 6 publications

References 8 publications

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

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

Automotive aeroacoustics: An overview

Numerical investigation on the contribution of underbody flow-induced noise on vehicle interior noise

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