Validation of Occupant Trajectory Model using the Ford Expedition Dolly Rollover Experimental Test Data

Paper #:
  • 2013-01-0472

  • 2013-04-08
Hovey, C., Raphael, E., and Xu, H., "Validation of Occupant Trajectory Model using the Ford Expedition Dolly Rollover Experimental Test Data," SAE Technical Paper 2013-01-0472, 2013,
In 2008, Hovey et al. [1] published a mathematical analysis that, for the first time, incorporated yaw data into the trajectory analysis, yielding occupant ejection results that are three-dimensional. We extend on that work to investigate the ability of the mathematical model to predict outcomes of the Ford Expedition dolly rollover test, details of which have been published in Carter et al. [2], Exponent [3], and Luepke et al. [4]. This research validates our occupant trajectory model with the Expedition dolly rollover experimental test data.Previous research had modeled the Expedition rollover test as a two-dimensional, straight-line, roll-only configuration, assuming yaw effects were negligible [17]. We incorporated the longitudinal and lateral components of the vehicle trajectory, eliminating the straight-line limitation. Moreover, we included yaw in addition to roll. Both enhancements resulted in a higher fidelity occupant ejection description. Novel to this work is the inclusion of experimental data from the dolly rollover test performed by Exponent [3] and analyzed by Carter et al. [2] to validate the model developed by Hovey et al. [1].Also novel to this work is the application of case-specific vehicle speed and roll rate data from the Expedition dolly rollover test. Previous efforts with a two-dimensional ejection analysis have modeled rollover events with the same roll rate function [17], thereby homogenizing the underlying uniqueness of a particular accident. Every rollover accident has a distinct signature, embodied in its translational and rotational degrees of freedom. We capture this uniqueness with case-specific, experimental data.The results of this effort indicate our model [1] predicts experimentally obtained test results. This validation provides analysts further confidence in the use of the previously developed model.
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