Load Identification for CAE Based Fatigue Life Prediction of a New Bus Type 2007-01-4281
Whereas for passenger cars CAE methods in the durability development process are meanwhile standard, for lower volume production vehicle types CAE methods have not really penetrated the durability oriented development process. Special buses like coaches can be seen in this category. This presentation is based on a common project of TEMSA and LMS International and describes the load identification process for a CAE based fatigue life prediction for a new bus type. The project covers the complete CAE based durability process for a development of a new vehicle type:
Evaluation of customer usage profile (CUP) for the target market by performing customer oriented long distance measurements
Test track measurement on a commercial test track (IDIADA/Spain)
Representation of the CUP by setting up a test schedule using strongly condensed test track events of a commercial test track (IDIADA)
Virtual load prediction via Multi-Body-Simulation (MBS) of the unconstrained full vehicle including the flexible bus structure and the active suspension control using the Hybrid Road Approach
CAE based fatigue life prediction of the complete bus structure
The presentation mainly concentrates on the load identification parts.
The special challenge in this project was to setup a process for load prediction which finds a good compromise between effort, accuracy, and sustainability. On the one hand lower volume vehicle producers have the problem that the development teams are relatively small, so that it is necessary to setup CAE processes which are manageable by these teams. On the other hand, from the viewpoint of smaller budgets, CAE methods are needed, which are able to do load prediction for vehicle variants without the necessity to do expensive measurements for every new vehicle type. The LMS Hybrid Road method, which does a back-calculation to the effective road profile based on an initial measurement with one vehicle specimen, fulfils this requirement best.