An Interval Analysis and Optimization Method for Generated Axial Force of Automotive Drive Shaft Systems

2020 ◽  
Author(s):  
Huayuan Feng ◽  
Subhash Rakheja
Author(s):  
Huayuan Feng ◽  
Subhash Rakheja ◽  
Wen-Bin Shangguan

The drive shaft system with a tripod joint is known to cause lateral vibration in a vehicle due to the axial force generated by various contact pairs of the tripod joint. The magnitude of the generated axial force, however, is related to various operating factors of the drive shaft system in a complex manner. The generated axial force due to a drive shaft system with a tripod joint and a ball joint was experimentally characterized considering ranges of operational factors, namely, the input toque, the shaft rotational speed, the articulation angle, and the friction. The data were analyzed to establish an understanding of the operational factors on the generated axial force. Owing to the observed significant effects of all the factors, a multibody dynamic model of the drive shaft system was formulated for predicting generated axial force under different operating conditions. The model integrated the roller–track contact model and the velocity-based friction model. Based on a quasi-static finite element model, a new methodology was proposed for identifying the roller–track contact model parameters, namely, the contact stiffness and force index. To further enhance the calculation accuracy of the multibody dynamic model, a new methodology for identifying the friction model parameters and the force index was proposed by using the measured data. The validity of the model was demonstrated by comparing the model-predicted and measured magnitudes of generated axial force for the ranges of operating factors considered. The results showed that the generated axial force of the drive shaft system can be calculated more accurately and effectively by using the identified friction and contact parameters in the paper.


Author(s):  
Jie Zhang ◽  
Qidong Wang ◽  
Han Zhang ◽  
Min Zhang ◽  
Jianwei Lin

Abstract In this study, a systematic optimization method for the thermal management problem of passenger vehicle was proposed. This article addressed the problem of the drive shaft sheath surface temperature exceeded allowable value. Initially, the causes and initial measures of the thermal problem were studied through computational fluid dynamics (CFD) simulation. Furthermore, the key measures and the relevant parameters were determined through Taguchi method and significance analysis. A prediction model between the parameters and optimization objective was built by radial basis function neural network (RBFNN). Finally, the prediction model and particle swarm optimization (PSO) algorithm were combined to calculate the optimal solution, and the optimal solution was selected for simulation and experiment verification. Experiment results indicated that this method reduced the drive shaft sheath surface temperature promptly, the decreasing amplitude was 22%, which was met the experimental requirements.


2007 ◽  
Vol 19 (3) ◽  
pp. 209-226 ◽  
Author(s):  
S. Serveto ◽  
J.-P. Mariot ◽  
M. Diaby
Keyword(s):  

2014 ◽  
Vol 28 (10) ◽  
pp. 4005-4010 ◽  
Author(s):  
Kwang-Hee Lee ◽  
Deuk-Won Lee ◽  
Jin-Ho Chung ◽  
Won-Oh Cho ◽  
Chul-Hee Lee

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