Load Simulation of the Impact Road under Durability and Misuse Conditions 2023-01-0775
Road load data is an essential input to evaluate vehicle durability and strength performances. Typically, load case of pothole impact constitutes the major part in the development of structural durability. Meanwhile, misuse conditions like driving over a curb are also indispensable scenarios to complement impact strength of vehicle structures.
This paper presents a methodology of establishing Multi-body Dynamics (MBD) full vehicle model in Adams/Car to acquire the road load data for use in durability and strength analysis. Furthermore, load level between durability and misuse conditions of the same Impact road was also investigated to explore the impact due to different driving maneuvers.
Curved Regular Grid (CRG) road model established by the help of Mobile Road Scanning (MRS) system, Flexible Ring Tire (FTire) model verified by a virtual tire test rig, dynamic deflection-force bump stop model acquired by the drop test rig and Craig-Bampton flexible vehicle body model transformed from simplified FEA entity were integrated in Adams/Car to fulfill the road load simulation task. In order to check the accuracy, a real Road Load Data Acquisition (RLDA) test was carried out by a SUV equipped with Six-axes Wheel Force Transducer (WFT) and certain acceleration sensors.
In both durability and misuse conditions, the simulation results are in good agreement with measured data, thus showing a good acceptance of the method. Compared to durability condition, most load levels are increased in misuse condition, but the ratio of increase is different. Wheel center longitudinal force shows a 5.4 times increase while the vertical has not even achieve 2 times. Vertical acceleration of wheel center shows a dramatic 4 times increase, but the amount of body comes to a more aggressive of 7.6 times.
Citation: Zhou, S., Yu, H., Zhu, C., and Liu, H., "Load Simulation of the Impact Road under Durability and Misuse Conditions," SAE Technical Paper 2023-01-0775, 2023, https://doi.org/10.4271/2023-01-0775. Download Citation
Author(s):
Shuai Zhou, Huili Yu, Caichao Zhu, Huaiju Liu
