Bharath Koya

A finite element model of a six-year-old child for simulating pedestrian accidents

Child pedestrian protection deserves more attention in vehicle safety design since they are the most vulnerable road users who face the highest mortality rate. Pediatric Finite Element (FE) models could be used to simulate and understand the pedestrian injury mechanisms during crashes in order to mitigate them. Thus, the objective of the study was to develop a computationally efficient (simplified) six-year-old (6YO-PS) pedestrian FE model and validate it based on the latest published pediatric data. The 6YO-PS FE model was developed by morphing the existing GHBMC adult pedestrian model. Retrospective scan data were used to locally adjust the geometry as needed for accuracy. Component test simulations focused only the lower extremities and pelvis, which are the first body regions impacted during pedestrian accidents. Three-point bending test simulations were performed on the femur and tibia with adult material properties and then updated using child material properties. Pelvis impact and knee bending tests were also simulated. Finally, a series of pediatric Car-to-Pedestrian Collision (CPC) were simulated with pre-impact velocities ranging from 20km/h up to 60km/h. The bone models assigned pediatric material properties showed lower stiffness and a good match in terms of fracture force to the test data (less than 6% error). The pelvis impact force predicted by the child model showed a similar trend with test data. The whole pedestrian model was stable during CPC simulations and predicted common pedestrian injuries. Overall, the 6YO-PS FE model developed in this study showed good biofidelity at component level (lower extremity and pelvis) and stability in CPC simulations. While more validations would improve it, the current model could be used to investigate the lower limb injury mechanisms and in the prediction of the impact parameters as specified in regulatory testing protocols.

Driver injury risk variability in finite element reconstructions of Crash Injury Research and Engineering Network (CIREN) frontal motor vehicle crashes

Objective: A 3-phase real-world motor vehicle crash (MVC) reconstruction method was developed to analyze injury variability as a function of precrash occupant position for 2 full-frontal Crash Injury Research and Engineering Network (CIREN) cases. Method: Phase I: A finite element (FE) simplified vehicle model (SVM) was developed and tuned to mimic the frontal crash characteristics of the CIREN case vehicle (Camry or Cobalt) using frontal New Car Assessment Program (NCAP) crash test data. Phase II: The Toyota HUman Model for Safety (THUMS) v4.01 was positioned in 120 precrash configurations per case within the SVM. Five occupant positioning variables were varied using a Latin hypercube design of experiments: seat track position, seat back angle, D-ring height, steering column angle, and steering column telescoping position. An additional baseline simulation was performed that aimed to match the precrash occupant position documented in CIREN for each case. Phase III: FE simulations were then performed using kinematic boundary conditions from each vehicles event data recorder (EDR). HIC15, combined thoracic index (CTI), femur forces, and strain-based injury metrics in the lung and lumbar vertebrae were evaluated to predict injury. Results: Tuning the SVM to specific vehicle models resulted in close matches between simulated and test injury metric data, allowing the tuned SVM to be used in each case reconstruction with EDR-derived boundary conditions. Simulations with the most rearward seats and reclined seat backs had the greatest HIC15, head injury risk, CTI, and chest injury risk. Calculated injury risks for the head, chest, and femur closely correlated to the CIREN occupant injury patterns. CTI in the Camry case yielded a 54% probability of Abbreviated Injury Scale (AIS) 2+ chest injury in the baseline case simulation and ranged from 34 to 88% (mean = 61%) risk in the least and most dangerous occupant positions. The greater than 50% probability was consistent with the case occupants AIS 2 hemomediastinum. Stress-based metrics were used to predict injury to the lower leg of the Camry case occupant. The regional-level injury metrics evaluated for the Cobalt case occupant indicated a low risk of injury; however, strain-based injury metrics better predicted pulmonary contusion. Approximately 49% of the Cobalt occupants left lung was contused, though the baseline simulation predicted 40.5% of the lung to be injured. Conclusions: A method to compute injury metrics and risks as functions of precrash occupant position was developed and applied to 2 CIREN MVC FE reconstructions. The reconstruction process allows for quantification of the sensitivity and uncertainty of the injury risk predictions based on occupant position to further understand important factors that lead to more severe MVC injuries.

Numerical investigation of driver lower extremity injuries in finite element frontal crash reconstruction

ABSTRACT Objective: Lower extremity injuries are the most frequent Abbreviated Injury Scale (AIS) 2 injury for drivers in frontal crashes. The objective was to reconstruct 11 real-world motor vehicle crashes (2 with AIS 2+ distal lower extremity injury and 9 without lower extremity injury) and to analyze the vehicle parameters and driver attributes that affect injury risk. Methods: Eleven frontal crashes were reconstructed with a finite element simplified vehicle model (SVM) using a semi-automated optimization method. The SVM was tuned to each corresponding vehicle and the Total HUman Model for Safety (THUMS) Ver 4.01 was scaled and positioned in a baseline configuration to mimic the documented precrash driver posture. The event data recorder crash pulse was applied as the boundary condition for each case. Additionally, for the 2 cases with lower extremity injury, 120 simulations to quantify the uncertainty and response variation were performed varying the following parameters using a Latin hypercube design of experiment (DOE): seat track position, seatback angle, steering column angle, steering column position, and D-ring height. Injury metrics implemented within THUMS were calculated from the femur, tibia, and ankle and cross-compared among the 11 baseline cases using tibia index and multiple injury risk functions. Kinetic and kinematic data from the 120-simulation DOE were analyzed and fit to regression models to examine any causal relationship between occupant positioning and lower extremity injury risk. Results: Of the 11 real-world crashes, both cases with lower extremity injuries resulted in elevated tibia axial forces and resultant bending moments, compared to the 9 cases without lower extremity injury. The average tibia index of the 2 cases with distal lower extremity injury (left: 1.79; right: 1.19) was higher than that in the 9 cases without lower extremity injury (left: 1.16, P =.024; right: 0.82, P =.024). An increased risk of AIS 2+ tibia shaft (33.6%), distal tibia and hindfoot (20.0%), as well as ankle malleolar (14.5%) fracture was also observed for the injured compared to the noninjured cases. Rearward seat track position, reclined seat back angle, and reduced seat height were correlated with elevated tibia axial force and increased tibia index, imposing additional lower extremity injury risk. Conclusions: This study provides a computational framework for assessing lower extremity injuries and elucidates the effect of precrash driver posture on lower extremity injury risk while accounting for vehicle parameters and driver attributes. Results from the study aid in the evaluation of real-world injury data, the understanding of factors contributing to injury risk, and the prevention of lower extremity injuries.

A Finite Element Model of a mid-size male for simulating pedestrian accidents

Pedestrians represent one of the most vulnerable road users and comprise nearly 22% the road crash-related fatalities in the world. Therefore, protection of pedestrians in car-to-pedestrian collisions (CPC) has recently generated increased attention with regulations involving three subsystem tests. The development of a finite element (FE) pedestrian model could provide a complementary component that characterizes the whole-body response of vehicle-pedestrian interactions and assesses the pedestrian injuries. The main goal of this study was to develop and to validate a simplified full body FE model corresponding to a 50th male pedestrian in standing posture (M50-PS). The FE model mesh and defined material properties are based on a 50th percentile male occupant model. The lower limb-pelvis and lumbar spine regions of the human model were validated against the postmortem human surrogate (PMHS) test data recorded in four-point lateral knee bending tests, pelvic\abdomen\shoulder\thoracic impact tests, and lumbar spine bending tests. Then, a pedestrian-to-vehicle impact simulation was performed using the whole pedestrian model, and the results were compared to corresponding PMHS tests. Overall, the simulation results showed that lower leg response is mostly within the boundaries of PMHS corridors. In addition, the model shows the capability to predict the most common lower extremity injuries observed in pedestrian accidents. Generally, the validated pedestrian model may be used by safety researchers in the design of front ends of new vehicles in order to increase pedestrian protection.

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

ABSTRACT Objective: 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. Methods: 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. Results: Kinematics between both methods were similar (peak max deviation: ΔXhead = 17 mm; ΔZhead = 4 mm; ΔXthorax = 5 mm; ΔZthorax = 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. Conclusions: 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.

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

ABSTRACT Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

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

ABSTRACT 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.