1638 / Analysis of tire characteristics on road surface with volcanic ash fall

Paper presented at the 16th European-African Regional Conference of the ISTVS


Title: Analysis of tire characteristics on road surface with volcanic ash fall

Authors: Junya Yamakawa and Ryosuke Eto

Abstract: Mt. Fuji in Japan has erupted repeatedly in the past in cycles of about 100 years. Although there are currently no signs of eruption, it is undeniable that an eruption will occur in the near future. An eruption could produce 2-10 cm of ash fall in the Tokyo metropolitan area, which is about 100 km away from Mt. Fuji. An event was held in which asphalt paved parking lots were covered with volcanic ash to allow citizens to experience how driving vehicles would be affected by the ash fall caused by the volcanic eruption.We collected data by driving an instrumented vehicle on the volcanic ash roads at this event site. In this presentation, we describe the results obtained by analyzing the data collected on the simulated ash roads. It is relatively easy to see the characteristics of longitudinal tire force, i.e., rolling resistance, driving force, or braking force, because, like friction force, they can be expressed as dimensionless quantities divided by the vertical load. However, the tire side force generated during vehicle turning is largely due to the sideslip angle, but it is also affected by the camber angle, which is the inclination of the tire from the vertical plane, as well as the vertical load. Since the side force data during driving is collected while the values of not only the lateral slip angle but also the ground load and camber angle are changing, it is necessary to determine the relationship between these variables and the side force. For this reason, we analyzed the side force data using a Gaussian process, which allows regression on multiple variables. For Gaussian process regression, since it takes time to find appropriate values for the hyper-parameters of the kernel, we also attempted to model the process using a neural network.

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