CAE-based Virtual Shaker Table for Exhaust System Component Development

2016-01-1362

04/05/2016

Event
SAE 2016 World Congress and Exhibition
Authors Abstract
Content
Traditionally, fatigue calculations are based on the time domain approach. Acceleration time history inputs are used to excite the system. Through the element stress time history output and rainflow cycle count algorithm, fatigue damage can be calculated through the Palmgren-Miner cumulative damage rule. Nevertheless, it can be a daunting process for CAE analysts as it requires iteration for each individual event in the schedule before calculating the fatigue life. The alternative approach is frequency domain fatigue calculation. In this approach, both the dynamic loading and response are expressed in terms of Power Spectral Density (PSD) functions and the dynamic structure is treated as a linear transfer function. The stress PSD is then obtained by multiplying the transfer function with the PSD load.
The objective of this paper is to present a CAE based virtual shaker table procedure for an automotive exhaust component and subjecting it to PSD for fatigue life prediction. In the paper, Nastran frequency response analysis is employed to extract system response as a transfer function that combines with nCode DesignLife and PSD inputs for fatigue life estimation. A case study of an exhaust after-treatment component, a diesel compact mixer, is given to demonstrate using this procedure. The fatigue life calculation of the component is performed based on multiple algorithms and the results show that Dirlik and Lalanne methods give the best correlation with the physical shaker test. It also illustrates the effectiveness of the CAE virtual validation as a tool to quickly identify the critical areas in the system during the early stage of product development cycle.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-1362
Pages
5
Citation
Shih, H., and Chen, Y., "CAE-based Virtual Shaker Table for Exhaust System Component Development," SAE Technical Paper 2016-01-1362, 2016, https://doi.org/10.4271/2016-01-1362.
Additional Details
Publisher
Published
Apr 5, 2016
Product Code
2016-01-1362
Content Type
Technical Paper
Language
English