Experimental study on wheel-soil interaction mechanics using in-wheel sensor and particle image velocimetry part II. Analysis and modeling of shear stress of lightweight wheeled vehicle. (October 2020)

• Record Type: Journal Article
• Authors: Horiko, Shota; Ishigami, Genya
• Is Part Of: Journal of terramechanics. Volume 91(2020)
• Page Start: 243
• Page End: 256
• Publication Date: 2020-10
• Subjects: Wheel-soil interaction -- Shear stress -- In-wheel sensor -- Particle image velocimetry; Trafficability -- Periodicals; Praticabilité (Routes) -- Périodiques; Trafficability; Periodicals; 629.222
• DOI: 10.1016/j.jterra.2020.07.002
• Languages: English
• ISSNs: 0022-4898

Abstract

Highlights: The shear stress distribution and soil flow beneath a rigid wheel on loose sand are comprehensively measured. The shear stress model carefully considers frictional characteristics of granular media. Simulation study for the wheel driving characteristics confirms the accuracy of the proposed model. Abstract: Wheeled vehicle mobility on loose sand is highly subject to shear deformation of sand around the wheel because the shear stress generates traction force of the wheel. The main contribution of this paper is to improve a shear stress model for a lightweight wheeled vehicle on dry sand. This work exploits two experimental approaches, an in-wheel sensor and a particle image velocimetry that precisely measure the shear stress and shear deformation generated at the interaction boundary. Further, the paper improves a shear stress model. The model proposed in this paper considers a force chain generated inside the granular media, boundary friction between the wheel surface and sand, and velocity dependency of the friction. The proposed model is experimentally validated, and its usefulness is confirmed through numerical simulation of the wheel traction force. The simulation result confirmed that the proposed model calculated the traction force with an accuracy about 70%, whereas the conventional one overestimated the force, and its accuracy was 13% at the best.