Successful race teams rely heavily on track testing to search for the ideal suspension setup. As more restrictions are placed on the amount of on-track testing by major racing sanctioning bodies, such as NASCAR, teams have increased their attention to alternate testing methods to augment their track data and better understand the dynamics of their racecars. One popular alternate to track testing is 8-post dynamic shaker rig testing. Eight-post rig testing gives the team a better understanding of the vehicle's dynamics before they arrive at the race track, allowing them to use their limited track testing time more efficiently.

While 8-post rig testing certainly is an attractive option, an extensive test matrix is often required to find the best suspension setups. To take full advantage of 8-post rig tests, more efficient experimental methods are needed. Since investigating shock absorber selection is often the most time-consuming task, this study focuses on developing more efficient methods to select the best shock absorber setups.

This study develops a novel method that applies dynamic substructuring and system identification to generate a mathematical model that predicts the results of future tests as both command inputs and components are changed. This method is used to predict the results of 8-post rig tests as actuator commands and shock absorber forces are varied. The resulting model can then be coupled with shock absorber models to simulate how the vehicle response changes with shock absorber selection. This model can then be applied to experimental design.

First, a physically-motivated nonlinear dynamic shock absorber model is developed, suitable for quickly fitting experimental data and implementing in simulation studies. Next, a system identification method to identify a vehicle model using experimental data is developed. The vehicle model is then used to predict response trends as shock absorber selection is varied. Comparison of simulation and experimental results show that this model can be used to predict the response levels for 8-post rig tests and aid in streamlining 8-post rig testing experimental designs.