Development of a Multi-Body Dynamic Model of a Tractor-Semitrailer for Ride Quality Prediction
Date Published: 2001-11-12
Paper Number:2001-01-2764
DOI: 10.4271/2001-01-2764
Citation:
Anderson, D., Schade, G., Hamill, S., and O'Heron, P., "Development of a Multi-Body Dynamic Model of a Tractor-Semitrailer for Ride Quality Prediction," SAE Technical Paper 2001-01-2764, 2001, doi:10.4271/2001-01-2764.
Increasingly, manufacturers are looking to computer simulation methods to accurately assess ride quality potential of new vehicle designs as they are being developed. This requires detailed multi-body dynamic models to be developed with sufficient fidelity to replicate ride relevant phenomenon. These models must have the capability to:
Represent the distributed mass and elasticity of the vehicle structures (e.g. frame ladder, cab, and trailer).
Include the non-linear behavior of shock absorbers and elastomeric components.
Reproduce the fundamental system dynamics that influence ride.
Provide output of the acceleration, velocity, and displacement measures needed to compute ride quality.
This paper discusses the development of an ADAMS multi-body dynamic model of a tractor semi-trailer for use as a predictive tool in evaluating ride quality design improvements. The model includes flexible representations of the frame, cab, and trailer generated from finite element models through the use of component mode synthesis. Non-linearities and viscoelastic effects associated with compliant elements and dampers are selectively incorporated using explicit functions and spline/surface interpolation. Road inputs into the suspension are generated via tire elements in contact with road profiles or by prescribing axle motions. Additionally, a ride quality algorithm is built into the model allowing virtual ride measurements to be taken and compared to physical test results.
Construction and correlation of the model followed a multi-step process in which each of the major subsystems were built and validated to test results prior to incorporation in the full vehicle model. Ride data from a vehicle instrumented with 70 transducers was collected on highway routes and used to support full vehicle correlation.
The ability of the model to predict ride quality variations due to changes in vehicle configuration was tested by evaluating several case studies. These results are presented along with details of the model construction and correlation process.
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