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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.

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.