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Our goal was to evaluate whether modifications to the force-displacement curves derived from a high-speed NHTSA frontal barrier test could be used to improve predictions of the equivalent barrier speed of a low-speed crash involving the same vehicle. Using an earlier iteration of the technique described here, Hunter et al. [2] showed that the F-D curves from higher-speed tests over-predicted the EBS of lower-speed tests by 21±17%. After modifying the earlier technique to account for powertrain stack-up and barrier force attenuation prior to reaching peak dynamic crush, the technique evaluated here reduced this error to 1% with a standard deviation that varied between ±9% and ±13% depending on which engine accelerometers were chosen for the adjustment. These findings suggest that the method and modifications proposed here can be used to reconstruct car crashes provided that there is a relationship between dynamic crush and residual crush.

The objective of this study was to assess the accuracy of using high-speed frontal barrier crash tests to predict the impact speed, i.e. equivalent barrier speed (EBS), of a lower-speed frontal barrier crash. Force-displacement (F-D) curves were produced by synchronizing the load cell barrier (LCB) data with the accelerometer data. Our analysis revealed that the F-D curves, including the rebound phase, for the same vehicle model at the same impact speed were generally similar. The test vehicle crush at the time of barrier separation, determined from the F-D curves, was on average 17±16% (N = 150) greater than the reported maximum hand-measured residual crush to the bumper cover. The EBS calculated from the F-D curves was on average 4±4% (N=158) greater than the reported EBS, indicating that using F-D curves derived from LCB data is a reliable method for calculating vehicle approach energy in a crash test.

PC-Crash simulations of staged collisions require dozens of parameters describing vehicle and impact parameters. The Collision Optimizer will vary initial speeds and impact parameters to obtain a best fit to a desired end state, but vehicle parameters are left unchanged. The present paper allows these other parameters to vary in thousands of combinations, re-optimizing the solution in each to find the relationships between the previously fixed parameters and the resulting impact speeds. The results show that tire friction and vehicle inertial properties have the most influence on impact speeds. Other parameters have little influence on the results.