Conference & Tech Papers

Ride Quality Prediction of a Tractor-Semitrailer

Conference & Tech Papers ID    PA406
Status:    Published
Published date:    04/27/2017
Updated:    04/27/2017
Reported In:   Adams


David Anderson, Gregory Schade; International Truck and Engine Corp.


Technical Paper

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, trailer)
·     include the non-linear behavior of shock absorbers and elastomeric components
·     reproduce the fundamental system dynamics which 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-semitrailer for use as a predictive tool in evaluating ride quality design improvements.  The model includes flexible representations of the frame, cab, and trailer imported from finite element code 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.  The results from one case are presented along with details of the model construction and correlation process.


English Attachments

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