Browse Publications Technical Papers 2009-01-2087
2009-05-19

Optimizing R&H and NVH Performances Early in the Design Process via Multi Body Simulation 2009-01-2087

This paper presents a CAE based approach to accurately simulate and optimize Ride and Handling metrics. Because of the wide range of vehicle phenomena involved, across the variety of frequency ranges, it is essential that the vehicle model includes proper representation of the dynamic properties of the various subsystems (e.g. tires, steering, PT, etc.) Precise correlation between test and simulation for standalone vehicle components and systems is achieved by replicating in the MBS (Multi-body Simulation) the same tests and boundary conditions. This allows the analyst to correctly define those crucial elements and parameters which have the greatest effect on the R&H attribute to be investigated.
Setting up the simulation to correctly represent only one single maneuver simulation at a time would not allow the analyst to consider how the dynamic properties of the chassis design variables should be tuned to achieve to best balance and trade-offs. Instead, it is necessary to consider all the metrics in an automatic process, and to define a single run which uses a more global sensitivity/optimization loop and considers all of the Handling and Ride Comfort performances at once.
This simulation process involves a very large number of complex calculations. In order to be efficient and practical, it is necessary to consider ways to optimize the calculation time of the simulation. The best compromise between simulation time and accuracy of the predicted indices can be selected by various modeling simplifications such as flexible body modes reductions, track segmentation, etc.). In this paper a completely automatic procedure for simulating and extracting Handling & Ride Comfort Metrics will be presented, along with the methodology to simulate a variety of maneuvers and phenomena in a looped process starting from a correlated and parameterized vehicle multi-body model.

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