Chassis Loads Prediction using Measurements as Input to an Unconstrained Multi-Body Dynamics Model
Automotive engineering development processes are growing more dependent on the use of multi-body dynamic (MBD) models for generating vehicle loads that at one time could only be measured using physical hardware. A certain technique combines these two approaches using a minimal set of physical measurements to excite a vehicle MBD model for predicting loads at various vehicle interfaces. This approach eliminates the use of a tire model, often the roadblock in MBD-based loads prediction simulations. However, for various reasons, the direct application of loads to a model can lead to problems with the simulation. Alternatively, the model can be artificially constrained but this also has its disadvantages. The purpose of this paper is to present a loads prediction technique that relaxes the use of artificial boundary conditions for applications involving the input of measurements to an MBD model.