Machine Learning-Based Prediction of Wind-Induced Interior Noise in Ground Vehicles

In response to increasing customer demand for enhanced passenger comfort and perceived vehicle quality, OEMs in automotive and commercial vehicles are placing significant emphasis on reducing the interior cabin noise. At highway speeds, wind noise is a primary contributor to the overall noise within the vehicle cabin. Conventional approaches to predict vehicle wind noise rely on physical testing, which can only be conducted in the later stages of the design process once a physical prototype is available. Increased adoption of established computational fluid dynamics (CFD) methods has enabled earlier assessment. However, such simulations require several hours to complete, posing a challenge in the context of rapid design iteration cycles. With the growing adoption of artificial intelligence in engineering, machine learning (ML) approaches have been proposed to predict a vehicle’s aerodynamics performance. Nevertheless, development of ML techniques in the context of aeroacoustics applications has not received as much attention. In this study, we propose a deep learning-based framework trained on CFD data to predict the sound pressure levels (SPL) at the driver’s headspace along with visualization of pressure and acoustic loads on relevant panels for noise source identification. The proposed model accurately predicts interior SPL across the frequency range of interest as shown by comparison to the reference CFD simulations. Moreover, prediction times are reduced from hours to seconds, enabling practical use in early stages of the vehicle design process and offering the ability to iterate faster to evaluate different scenarios.