Rapid prediction of vehicle interior wind noise via a hybrid convolutional neural network-transformer model and geometric styling features

Abstract

The rapid evaluation of interior aerodynamic noise during the Concept A Surface design stage is important for vehicle acoustic development, but conventional methods are limited by high cost and low efficiency. This study proposes a convolutional neural network transformer-based prediction method for vehicle interior wind noise by integrating vehicle styling features and acoustic technical parameters. An optimal Latin hypercube sampling method was used to generate design combinations, and wind tunnel tests were conducted at 120 km/h. Key vehicle styling parameters, including A-pillar geometry, side mirror dimensions, front windscreen angle, side mirror-to-body spacing, and side window inclination, together with glazing material properties, glass thickness, acoustic transfer function, and interior reverberation time, were selected as input features to predict the driver’s left-ear wind noise spectrum. Based on five-fold cross-validation, the proposed model was compared with convolutional neural network (CNN), long short-term memory (LSTM), and transformer models. The CNN-Transformer model achieved the best performance, with mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 2.23% and 0.94 dB, respectively. Compared with the transformer, CNN, and LSTM models, the proposed method reduced MAPE by 24.91%, 36.29%, and 49.59%, and reduced RMSE by 22.95%, 35.17%, and 48.07%, respectively. The model also maintained reliable performance on an independent test set, with MAPE and RMSE values of 4.82% and 1.44 dB. The mean impact value method was further applied to identify the influence of design parameters on interior wind noise, guiding for early vehicle acoustic optimization.