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Road loads tire models are used in the automotive industry in full vehicle simulations to compute the loading from the road into the chassis encountered in proving ground durability events. Such events typically include Belgian Block events, bump events, potholes and others. Correctly capturing tire enveloping forces in such events has historically been challenging - several different approaches exist each with its own limitations. In this paper a model is presented which captures the first order tire dynamics (frequencies lower than 80 Hz) and associated enveloping loading without the need of an effective road profile. The theory behind this tire model is briefly introduced. Importantly, a comprehensive study of the validation of the tire model is given which shows correlation for full vehicle dynamic proving ground events. A Virtual Tire Lab (VTL) pre-processing tool is also presented which is used to compute tire model input parameters from a validated non-linear FEA tire model.

A backpropagation through time algorithm was used to model and predict the rollover of a tank truck carrying varying liquid volumes, traveling at various speeds, and performing a number of steering maneuvers of up to 12 seconds duration. The training and testing data sets were built with data produced by simulations using first principle models. Because neural networks have trouble predicting behaviors beyond the boundaries of their training sets, the training set was weighted with 5 per cent of the input examples involving vehicle rollover due to sloshing. The network outputs under test data sets produced very strong correlations with first principle roll simulations in both rollover and non-extreme steering maneuvers.