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CAE Predictions for Cardan Joint Induced Driveline NVH
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Automotive vehicles equipped with Cardan joints may experience low frequency vehicle launch shudder vibration (5-30Hz) and high frequency driveline moan vibration (80-200Hz) under working angles and speeds. The Cardan joint introduces a 2nd order driveshaft speed variation and a 4th order joint articulation torque (JAT) causing the vehicle shudder and moan NVH issues. Research on the Cardan joint induced low frequency vehicle shudder using a Multi-Body System (MBS) method has been attempted. A comprehensive MBS method to predict Cardan joint induced high frequency driveline moan vibration is yet to be developed. This paper presents a hybrid MBS and Finite Element Analysis (FEA) approach to predict Cardan joint induced high frequency driveshaft moan vibration. The CAE method considers the elastically coupled driveshaft bending and engine block vibration due to Cardan joint excitation. Detailed driveshaft, joints, slip mechanism, differential, axle and wheels were modeled using a MBS modeling tool. The FEA engine block model was imported using the Craig-Bampton method. The CAE driveshaft bending frequency was verified with the Euler-Bernoulli beam equation and with a driveshaft impact test. CAE order cut vibrations at driveline attachment points were correlated with Dyno test-rig measurements as well as with vehicle test data under various operating conditions. This method was used to optimize the Cardan joint induced driveshaft moan performance up front in the development process, with the benefits of reducing hardware testing needs, avoiding late issues, and allowing a more cost effective design to be explored before hardware prototypes were built.
CitationLiu, J., Remisoski, N., Iqbal, J., and Egenolf, R., "CAE Predictions for Cardan Joint Induced Driveline NVH," SAE Technical Paper 2017-01-1136, 2017, https://doi.org/10.4271/2017-01-1136.
Data Sets - Support Documents
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