A preliminary study of human model head and neck response to frontal loading in nontraditional occupant seating configurations

<p><b>Objective</b>: Computational human body models (HBMs) are nominally omnidirectional surrogates given their structural basis in human anatomy. As a result, such models are well suited for studies related to occupant safety in anticipated highly automated vehicles (HAVs). We utilize a well-validated HBM to study the head and neck kinematics in simulations of nontraditional occupant seating configurations.</p> <p><b>Methods</b>: The GHBMC M50-O v. 4.4 HBM was gravity settled into a generic seat buck and situated in a seated posture. The model was simulated in angular increments of 15 degrees clockwise from forward facing to rear facing. A pulse of 17.0 kph (NASS median) was used in each to simulate a frontal impact for each of the 13 seating configurations. Belt anchor points were rotated with the seat; the airbag was appropriately powered based on delta-V, and was not used in rear-facing orientations. Neck forces and moments were calculated.</p> <p><b>Results</b>: The 30-degree oblique case was found to result in the maximum neck load and sagittal moment, and thus Neck Injury Criteria (NIJ). Neck loads were minimized in the rear facing condition. The moments and loads, however, were greatest in the lateral seating configuration for these frontal crash simulations.</p> <p><b>Conclusions</b>: In a recent policy statement on HAVs, the NHTSA indicated that vehicle manufacturers will be expected to provide countermeasures that will fully protect occupants given any planned seating or interior configurations. Furthermore, the agency indicated that virtual tests using human models could be used to demonstrate such efficacy. While the results presented are only appropriate for comparison within this study, they do indicate that human models provide reasonable biomechanical data for nontraditional occupant seating arrangements.</p

Failed rib region prediction in a human body model during crash events with precrash braking

<p><b>Objective</b>: The objective of this study is 2-fold. We used a validated human body finite element model to study the predicted chest injury (focusing on rib fracture as a function of element strain) based on varying levels of simulated precrash braking. Furthermore, we compare deterministic and probabilistic methods of rib injury prediction in the computational model.</p> <p><b>Methods</b>: The Global Human Body Models Consortium (GHBMC) M50-O model was gravity settled in the driver position of a generic interior equipped with an advanced 3-point belt and airbag. Twelve cases were investigated with permutations for failure, precrash braking system, and crash severity. The severities used were median (17 kph), severe (34 kph), and New Car Assessment Program (NCAP; 56.4 kph). Cases with failure enabled removed rib cortical bone elements once 1.8% effective plastic strain was exceeded. Alternatively, a probabilistic framework found in the literature was used to predict rib failure. Both the probabilistic and deterministic methods take into consideration location (anterior, lateral, and posterior). The deterministic method is based on a rubric that defines failed rib regions dependent on a threshold for contiguous failed elements. The probabilistic method depends on age-based strain and failure functions.</p> <p><b>Results</b>: Kinematics between both methods were similar (peak max deviation: Δ<i>X</i>_head = 17 mm; Δ<i>Z</i>_head = 4 mm; Δ<i>X</i>_thorax = 5 mm; Δ<i>Z</i>_thorax = 1 mm). Seat belt forces at the time of probabilistic failed region initiation were lower than those at deterministic failed region initiation. The probabilistic method for rib fracture predicted more failed regions in the rib (an analog for fracture) than the deterministic method in all but 1 case where they were equal. The failed region patterns between models are similar; however, there are differences that arise due to stress reduced from element elimination that cause probabilistic failed regions to continue to rise after no deterministic failed region would be predicted.</p> <p><b>Conclusions</b>: Both the probabilistic and deterministic methods indicate similar trends with regards to the effect of precrash braking; however, there are tradeoffs. The deterministic failed region method is more spatially sensitive to failure and is more sensitive to belt loads. The probabilistic failed region method allows for increased capability in postprocessing with respect to age. The probabilistic failed region method predicted more failed regions than the deterministic failed region method due to force distribution differences.</p