Search Results

Competitive pressures, globalization of markets, and integration of new materials and technologies into heavy vehicle suspension systems have increased demand for durability validation of new designs. Traditional Proving Ground and on-road testing for suspension development have the limitations of extremely long test times, poor repeatability and the corresponding difficultly in getting good engineering level data on failures. This test approach requires a complete vehicle driven continuously over severe Proving Ground events for extended periods. Such tests are not only time consuming but also costly in terms of equipment, maintenance, personnel, and fuel. Ideally multiple samples must be tested to accumulate equivalent millions of kilometers of operation in highly damaging environments.

The intention of this work is to illustrate a method used to overcome limitations of tire models developed during an evaluation study of an Empirical Dynamic™ (ED) damper model. A quarter vehicle test system was built to support the evaluation, and a model of the test system was also developed in ADAMS™. In the model, the damper was represented by a polynomial spline function and by an ED model separately. Vehicle level comparisons between the physical measurements and the model predictions were conducted. The actuator displacement signal from the physical test was used to drive the virtual test system. Spindle acceleration, spindle force, and other signals were collected for comparison. The tire model was identified as a significant source of error and as a result, the direct vehicle level correlation study did not illustrate any advantage of the ED damper model over a spline damper model.

This paper documents the process used to correlate the secondary belt degradation experienced on the test track with the secondary belt degradation experienced during laboratory tests using a Secondary Drive Test System. Two different software products were used to produce this correlation: nCode's pseudo-damage functionality was used to estimate the proportional belt degradation and MTS's RPC Pro functionality was used to edit the field data, create a time history file, and to shift the frequency domain of the vehicle into the usable range of the servo-hydraulic actuator (time stretching). For purposes of this paper, the test data and information presented in this paper is based on two different secondary drive belts that were used on the test track as well as in the laboratory tests. As will be shown, the plot information that resulted from these tests showed very good correlation.

Recent studies have shown that complex vehicle components such as shock absorbers, rubber bushings, and engine mounts can be accurately modeled by combining laboratory measurements with neural network technology. These nonlinear dynamic blackbox models (also known as Empirical Dynamics1 models) make it possible to predict nonlinear and hysteretic component behavior over wide ranges of amplitude and frequency. The models can handle realistic input waveforms as well as multiple inputs and multiple outputs. These techniques have now been applied to rolling pneumatic tires, to enable high accuracy predictions of tire and vehicle handling behavior. Models that predict high amplitude force components (three forces and three moments) using up to four randomly-varying inputs (radial deflection, slip angle, and camber angle, and slip ratio) have been successfully generated, using data obtained from MTS Flat-Trac III tire test equipment.

Hyundai Motor Company has combined physical and virtual testing tools to validate a full vehicle virtual prototype. Today a large number of physical tests are still required because the cycle of “design-build-test-change” relies on complex models of components and systems that typically are not easily validated. In order to shorten the development cycles, engineers perform multi-body simulations to dynamically excite components and systems and thereby estimate their durability under dynamic loads. The approach described herein demonstrates the feasibility of correlating the output from the corresponding physical and virtual prototype. Both synthetic and road load events are employed to excite physical and virtual vehicles, reveal difference in response, and ultimately improve the predictive capability of the model.

Brake groan noise and vibration occurs in a stopped vehicle by the simultaneous application of torque to the wheel and the gradual release of brake pressure. Eventually the torque load breaks the friction between pad and rotor causing slippage and energy release. If the torque load is not large enough to maintain slippage, a sustained stick-slip vibration, called groan, can occur which transmits a low frequency noise to the vehicle interior. In some cases the noise levels caused by groan can be objectionable, thus procedures for developing remedial designs are needed. To this end, a project was performed to analytically simulate groan vibration in a vehicle with a McPherson strut type suspension. The goal was to demonstrate that analytical models could be used to simulate groan behavior and to identify suspension components that affect the groan behavior. The ADAMS software was used to model a brake/suspension system.

The automotive vehicle design process has relied for many years on both analytical studies and physical testing. Testing remains to be required due to the inherent complexities of structures and systems and the simplifications made in analytical studies. Simulation test methods, i.e. tests that load components with forces derived from actual operating conditions, have become the accepted standard. Advanced simulation tools like iterative deconvolution methods have been developed to address this need. Analytical techniques, such as multi body simulation have advanced to the degree that it is practical to investigate the dynamic behavior of components and even full vehicles under the influence of operational loads. However, the approach of testing and analysis are quite unique and no seamless bridge between the two exists. This paper demonstrates an integrated approach to combine testing and analysis together in the form of virtual testing.

The paper addresses the connectivity issues related to integrating an Automated Model Based Calibration System (MTS Atlas) to a dynamometer test bed data acquisition system using an ASAM-GDI Interface. The GDI (Generic Device Interface) implementation was chosen over other ASAM interfaces due to its real-time capabilities and the ability to host new GDI drivers as these drivers become available. A structured migration process is developed showing how a new interface standard can be implemented that integrates with legacy test equipment, yet provides a simple low cost mechanism allowing replacement of old or redundant equipment.

This paper presents the results of a study of using a neural network black box model of a shock absorber of an ATV (All Terrain Vehicle, four wheel drive, off road, single person vehicle) for accurate load modeling. This study is part of a larger investigation into the dynamic behavior and associated fatigue of an ATV vehicle, which is conducted under the auspices of the Fatigue Design and Evaluation Committee of SAE of North America (www.fatigue.org). The general objectives are to develop new correlated methodologies that will allow engineers to predict the durability of components of proposed vehicles by means of a “digital prototype” simulation. Current state of the art multi body dynamics predictions use linear frequency response functions or non-linear polynomial approximations to describe the behavior of non-linear suspension components such as shock absorbers or bushings.

The objective of this work was to increase the effectiveness and efficiency of road simulation testing with an emphasis on obtaining more information from the laboratory test system. Attaining the objective was evaluated by the criteria: 1) was vehicle damage detected before a major failure, 2) were changes in test conditions that would result in over- or under-testing detected, 3) were vehicle and test system components that require maintenance detected and 4) did the changes detected provide a better understanding of the test specimen and analytical predictions. The tools used for this process were not integrated. An integrated set of tools would be required to make this a general-purpose technique