Leaf springs are essential elements in the suspension systems of vehicles including sport utility vehicles, trucks, and railroad vehicles. Accurate modeling of the leaf springs is necessary in evaluating ride comfort, braking performance, vibration characteristics, and stability. In order to accurately model the deformations and vibrations of the leaf springs, nonlinear finite-element procedures, which account for the dynamic coupling between different modes of displacement, are employed. Two finite-element methods that take into account the effect of the distributed inertia and elasticity are discussed in this investigation to model the dynamics of leaf springs. The first is based on a floating frame of reference formulation, while the second is an absolute nodal coordinate formulation. The floating frame of reference formulation allows for using a reduced-order model by employing component mode synthesis techniques, while the absolute nodal coordinate formulation enables more detailed finite-element models for the large deformation of very flexible leaf springs. Methods for modeling the contact and friction between the leaves of the spring are discussed. A comparison is also presented between the results obtained using the proposed method and simplified approaches presented in the literature. While there are many issues that can be important in leaf spring modeling, the analysis presented in this paper is focused on a few key issues that include the computer implementation, the effect of the dynamic load on the spring stiffness, the selection of the vibration modes in the reduced-order model, and the effect of the structural damping on the response of the leaf spring.
In current bridge maintenance practice, condition grades are assigned to individual bridges, based on regularly performed inspections. One of the main limitations to this approach is the subjective nature of grade assignment. To overcome this drawback, major bridge authorities are developing new methods for condition assessment based on collecting and evaluating sensor data. A major challenge in this context is to correctly model the impact of local deteriorations on the entire bridge's state. In this research, a system model-based approach has been developed to accurately model the correlations between the deterioration mechanisms and the measurement values indicating the progress of the deterioration. In addition, the system model describes the impact of the condition of individual bridge components on the condition of the overall bridge system. To this end, the bridge is hierarchically decomposed into modules, components and subcomponents, taking the structural system and mutual dependencies into account. The system model consists of three levels: The lowest level provides elements for modelling the input parameters provided by sensors or manual measurements. The mid-level models the deterioration mechanisms, taking the output of the parameter level into account. The topmost level models the structure of the bridge in a hierarchical manner, starting at the element parts up to the complete bridge system. The bridge´s condition is determined by state propagation mechanisms on the basis of logical elements connecting the aforementioned elements. In the end, the system model can be used to simulate the propagation of conditions assignments from the leaves (the sensors) to the top (the entire bridge). The developed system model approach is based on the application of the Systems Modelling Language (SysML). The paper will discuss in detail the advantages and limitations of the developed method and present a number of ostensive examples.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.