A Pre-warning Method for Cornering Speed of Concrete Mixer Truck 2020-01-1003
The high gravity center of the concrete mixer truck reduces the truck’s stability while steering, and the rolling stirring tank makes the stability even worse than the regular engineering vehicle due to the dynamic variation of centroid position. Most of the researches on the rollover stability of concrete mixer trucks focus on the rollover model establishment and dynamics simulation module. The influence of concrete centroid changes is ignored when the safe cornering speed is calculated. This paper proposes a pre-warning method for cornering speed of concrete mixer truck based on centroid dynamic simulation. In the method, the mixing tank stirring model and the vehicle driving dynamics model are established on the Fluent and TruckSim simulation platforms, respectively.The theoretical speed threshold obtained by simulation is used as the evaluation index of warning of the speed for steering. First, the dynamic simulation of the stirring tank model is carried out by Fluent. According to Newton Leibniz numerical calculation method, Matlab is used to obtain the mathematical model of the centroid position and the main parameters of the stirring tank. Then the model is verified by the neural network algorithm. Finally, according to the dynamic position of the vehicle’s centroid, the dynamic simulation is carried out by TruckSim to obtain the theoretical speed threshold. The pre-warning system can warn the driver according to the comparison of real-time speed and calculated velocity threshold. In this paper, a 15 concrete mixer truck is selected for simulation experiments. The results show that the lateral offset of the centroid is up to 110 mm under normal working conditions, and the safe turning speed of the vehicle is reduced by at least 3.82% due to the centroid change. The pre-warning method proposed in this paper can improve the safety of cornering traffic effectively , and can be utilized in the further intelligent transportation system.
Yifeng Jiang, Gangfeng Tan, Haoyu Wang, Zelong Wang, Zhenyu Wang, Ming Li
Wuhan University of Technology, Suizhou-WUT Industry Research Institute