Modular use of human body models of varying levels of complexity: Validation of head kinematics.

OBJECTIVE

The significant computational resources required to execute detailed human body finite-element models has motivated the development of faster running, simplified models (e.g., GHBMC M50-OS). Previous studies have demonstrated the ability to modularly incorporate the validated GHBMC M50-O brain model into the simplified model (GHBMC M50-OS+B), which allows for localized analysis of the brain in a fraction of the computation time required for the detailed model. The objective of this study is to validate the head and neck kinematics of the GHBMC M50-O and M50-OS (detailed and simplified versions of the same model) against human volunteer test data in frontal and lateral loading. Furthermore, the effect of modular insertion of the detailed brain model into the M50-OS is quantified.

METHODS

Data from the Navy Biodynamics Laboratory (NBDL) human volunteer studies, including a 15g frontal, 8g frontal, and 7g lateral impact, were reconstructed and simulated using LS-DYNA. A five-point restraint system was used for all simulations, and initial positions of the models were matched with volunteer data using settling and positioning techniques. Both the frontal and lateral simulations were run with the M50-O, M50-OS, and M50-OS+B with active musculature for a total of nine runs.

RESULTS

Normalized run times for the various models used in this study were 8.4 min/ms for the M50-O, 0.26 min/ms for the M50-OS, and 0.97 min/ms for the M50-OS+B, a 32- and 9-fold reduction in run time, respectively. Corridors were reanalyzed for head and T1 kinematics from the NBDL studies. Qualitative evaluation of head rotational accelerations and linear resultant acceleration, as well as linear resultant T1 acceleration, showed reasonable results between all models and the experimental data. Objective evaluation of the results for head center of gravity (CG) accelerations was completed via ISO TS 18571, and indicated scores of 0.673 (M50-O), 0.638 (M50-OS), and 0.656 (M50-OS+B) for the 15g frontal impact. Scores at lower g levels yielded similar results, 0.667 (M50-O), 0.675 (M50-OS), and 0.710 (M50-OS+B) for the 8g frontal impact. The 7g lateral simulations also compared fairly with an average ISO score of 0.565 for the M50-O, 0.634 for the M50-OS, and 0.606 for the M50-OS+B. The three HBMs experienced similar head and neck motion in the frontal simulations, but the M50-O predicted significantly greater head rotation in the lateral simulation.

CONCLUSION

The greatest departure from the detailed occupant models were noted in lateral flexion, potentially indicating the need for further study. Precise modeling of the belt system however was limited by available data. A sensitivity study of these parameters in the frontal condition showed that belt slack and muscle activation have a modest effect on the ISO score. The reduction in computation time of the M50-OS+B reduces the burden of high computational requirements when handling detailed HBMs. Future work will focus on harmonizing the lateral head response of the models and studying localized injury criteria within the brain from the M50-O and M50-OS+B.