Vehicle Wind Noise Prediction Based on Wasserstein Generative Adversarial Network and Squeeze-and-Excitation Xception Network

To address the challenge of efficiently evaluating vehicle aeroacoustic characteristics during the preliminary modeling stages, this article proposes an interior wind noise prediction method based on styling features and the squeeze-and-excitation module and Xception network. Drawing upon wind tunnel test data from sport utility vehicles of various dimensions operating under a 120 km/h condition, key styling parameters were identified based on vehicle geometry. Furthermore, to overcome the limitations associated with small sample sizes, a Wasserstein generative adversarial network is introduced to augment the original wind noise dataset. Subsequently, a complex nonlinear correlation is established between the acquired geometric parameters and the interior wind noise level. Validation results demonstrate that the proposed model achieved average MAPE values of 3.18%, 3.75%, and 3.26%, along with average RMSE values of 1.12 dB, 1.20 dB, and 1.19 dB, respectively, across three independent validation vehicles, indicating its promising efficiency and accuracy for vehicle wind noise evaluation. This research provides a streamlined and robust methodology to expedite the evaluation of interior wind noise, offering a practical technical reference for aerodynamic noise control during the preliminary styling stages.