Considering the importance of increasing driving safety, the study of safety is a popular and critical topic of research in the vehicle industry. Vehicle roll behavior with sudden steering input is a main source of untripped rollover. However, previous research has seldom considered road excitation and its coupled effect on vehicle lateral response when focusing on lateral and vertical dynamics. To address this issue, a novel method was used to evaluate effects of varying road level and steering wheel input on vehicle roll behavior. Then, a 9 degree of freedom (9-DOF) full-car roll nonlinear model including vertical and lateral dynamics was developed to study vehicle roll dynamics with or without of road excitation. Based on a 6-DOF half-car roll model and 9-DOF full-car nonlinear model, relationship between three-dimensional (3-D) road excitation and various steering wheel inputs on vehicle roll performance was studied. Finally, an E-Class (SUV) level car model in CARSIM^® was used, as a benchmark, with and without road input conditions. Both half-car and full-car models were analyzed under steering wheel inputs of 5°, 10° and 15°. Simulation results showed that the half-car model considering road input was found to have a maximum accuracy of 65%. Whereas, the full-car model had a minimum accuracy of 85%, which was significantly higher compared to the half-car model under the same scenario.

To address the urgent demand for high-performance and durable rotational blades in turbomachinery, e.g., aero-engine turbine blades operating under harsh environments and complex pulse loads, this study proposes a novel rotational sandwich plate model with a pre-set angle for investigating the dynamic responses of rotational composite turbine blades with graphene coating layers under pulse loads to capture both structural stagger angle effects and layered reinforcement mechanisms. A unique three-level discretization method of Chebyshev-Ritz-Galerkin is established to overcome the solving difficulty of partial differential governing equations (PDGEs), efficiently transforming them into numerically solvable ordinary differential governing equations (ODGEs), a key innovation that addresses the convergence challenge of traditional finite element methods (FEM) under high-speed rotation and large deformation and that ensures high computational efficiency and accuracy. Based on the first-order shear deformation theory and Hamilton principle, the model comprehensively integrates rotational centrifugal effects, material anisotropy of graphene-reinforced composites, and geometric nonlinearity. Three typical pulse loads, that is, step load, sinusoidal load, air blast load are considered to mimic real-world extreme working conditions, e.g., bird strikes, gusts, ice impacts. The modified Halpin-Tsai micromechanical model is adopted to accurately characterize the effective material properties of graphene-reinforced composites coating layers, while a systematic parametric study is conducted to reveal the effects of blade aspect ratio, graphene platelet (GPL) geometry length-to-thickness ratio, GPL weight fraction, rotational speed, damping, and excitation parameters on dynamic responses with a special focus on the synergistic effect of GPL weight fraction and rotational speed. Meanwhile, the integration of multi-physical field loads and graphene reinforcement to fill the gap in dynamic modeling of graphene-reinforced composites-based rotational blades under pulse loads. This work provides a robust theoretical framework and quantitative design guidelines for optimizing rotational composite turbine blades with graphene coating layers with superior vibration suppression, impact resistance, and fatigue performance, advancing the engineering application of advanced composite materials in high-speed turbomachinery.