Interior Wind Noise Prediction and Visual Explanation System for Exterior Vehicle Design Using Combined Convolution Neural Networks

Park, Haeun; Jung, Hoichan; Lee, Min Seok; Kim, Doohyung; Lee, Jongwon; Han, Sung Won

doi:10.1007/s12239-022-0088-9

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…However, machine learning methods have gained extensive application in automotive noise prediction. Commonly used machine learning methods include backpropagation neural networks (BPNNs) [39], radial basis neural networks [40], Elman neural networks [41], support vector machines [42,43], convolutional neural networks (CNNs) [44][45][46][47], and others. In recent years, machine learning methods have become a developmental trend in fluid dynamics prediction.…”

Section: Deep Learning For Wind Noise Predictionmentioning

confidence: 99%

“…Brunton [49] applied deep learning methods to capture the most relevant flow characteristics for predicting lift and drag on aircraft wings. A study by a university in South Korea [46] involved inputting images of a car from various angles into a deep learning model to predict the wind noise of modern car models. Meng et al [50], based on large-eddy simulation and acoustic analogy theory, utilized deep learning to predict the noise of a cylinder flow model using spatial coordinates and flow velocity as input.…”

Section: Deep Learning For Wind Noise Predictionmentioning

confidence: 99%

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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: Deep Learning For Wind Noise Predictionmentioning

confidence: 99%

Section: Deep Learning 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.

View full text Add to dashboard Cite

show abstract

Research on extraction and identification of automotive wind noise based on LSTM combined road and drum experiments

Huang,

Wang,

Cao

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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Wind noise is a significant source of noise when vehicles are driven at high speeds. While wind tunnel experiments are commonly method to study real automotive wind noise, anechoic wind tunnel laboratory are prohibitive for automakers on account of their high costs and limited experimental period. Therefore, this paper proposes to extract automotive wind noise through combining road experiment and drum experiment, define significant wind noise prime regions, and verify its effectiveness. Subsequently, the Long Short-Term Memory Neural Network algorithm (LSTM) is employed to reveal the complex nonlinear relationship between wind noise and its impact areas, in order to establish an identification model for automotive wind noise. The performance of the model is then compared with Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) models. The results indicate that the LSTM wind noise identification model exhibits higher accuracy, shorter training time, and stronger generalization ability, thus demonstrating the superiority of this model.

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Interior Wind Noise Prediction and Visual Explanation System for Exterior Vehicle Design Using Combined Convolution Neural Networks

Cited by 2 publications

References 17 publications

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

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

Research on extraction and identification of automotive wind noise based on LSTM combined road and drum experiments

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