Disk Model Effect for Road Surface Roughness Using Convolution Method

In most related studies on road surface roughness, the vehicle's wheel is often using a contact point model rather than a disk model. This results in neglecting the wheel's size and interaction with the road. Consequently, the vehicle's response may not be genuinely reflected, especially for the massive topic of noise, vibration, and harshness (NVH). Unlike the existing approach targeting the power spectrum, this paper proposes a new convolution method to tackle the disk effect and operates directly on the spatial domain, i.e. road surface roughness. By using a designed periphery function, it can simulate the wheel geometry passing through road surface roughness. The periphery function acts as a filter to the road surface roughness that can filter out smaller oscillations. Some examples involving roughness from ISO 8608 standards were tested. It is shown herein that the proposed method can match the theoretical result (using the geometry method (GM)) not only in the spatial domain but also in the power spectral density (PSD). Since the convolution is performed under the spatial domain, the proposed method can directly apply the disk model to any existing road surface roughness with different spectral compositions in practice. Understanding the disk effect reduces the higher frequency of the vehicle's response depending on roughness severity, which may significantly impact the vehicle design for ride comfort, road surface roughness extraction, bridge health monitoring using the drive-by method, etc.

Automated summary

This paper proposes a new convolution method to tackle the disk effect of the vehicle's wheel when encountering road surface roughness. The method can simulate the wheel geometry and filter out smaller oscillations using a designed periphery function. Experimental results show that the proposed method matches the theoretical results in both the spatial domain and the power spectral density. Understanding the disk effect is important for vehicle design, road surface roughness extraction, and bridge health monitoring.