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In the present study, various risk curves for moderate-to-fatal head injury (AIS2+) were theoretically assessed by comparing model-based injury rates with field-based injury rates. This was accomplished by applying the risk curves in corresponding field models. The resulting injury rates were considered from two perspectives: aggregate (0-56 kph events) and point-estimate (higher-speed, barrier-like events). Four risk curves were studied: a HIC15-based curve from Mertz et al. (1997), a BRIC-based curve from Takhounts et al. (2011), a BrIC-based curve from Takhounts et al. (2013) and a Concussion-Correlate-based curve from Rowson et al. (2013). The field modeling pertained to adult drivers in 11-1 o'clock, towaway, full-engagement frontal crashes in the National Automotive Sampling System (NASS, calendar years = 1993-2012), and the model-year range of the passenger vehicles was 1985-2010.

In the present study, transfer equations relating the responses of post-mortem human subjects (PMHS) to the mid-sized male Hybrid III test dummy (HIII50) under matched, or nearly-identical, loading conditions were developed via math modeling. Specifically, validated finite element (FE) models of the Ford Human Body Model (FHBM) and the HIII50 were used to generate sets of matched cases (i.e., 256 frontal impact cases involving different impact speeds, severities, and PMHS age). Regression analyses were subsequently performed on the resulting age-dependent FHBM- and HIII50-based responses. This approach was conducted for five different body regions: head, neck, chest, femur, and tibia. All of the resulting regression equations, correlation coefficients, and response ratios (PHMS relative to HIII50) were consistent with the limited available test-based results.

Transfer or response equations are important as they provide relationships between the responses of different surrogates under matched, or nearly identical loading conditions. In the present study, transfer equations for different body regions were developed via mathematical modeling. Specifically, validated finite element models of the age-dependent Ford human body models (FHBM) and the mid-sized male Hybrid III (HIII50) were used to generate a set of matched cases (i.e., 192 frontal sled impact cases involving different restraints, impact speeds, severities, and FHBM age). For each impact, two restraint systems were evaluated: a standard three-point belt with and without a single-stage inflator airbag. Regression analyses were subsequently performed on the resulting FHBM- and HIII50-based responses. This approach was used to develop transfer equations for seven body regions: the head, neck, chest, pelvis, femur, tibia, and foot.

A three-step process was developed to estimate fracture criteria for a human body model. The process was illustrated via example wherein skull fracture criteria were estimated for the Ford Human Body Model (FHBM)~a finite element model of a mid-sized human male. The studied loading condition was anterior-to-posterior, blunt (circular/planar) cylinder impact to the frontal bone. In Step 1, a conditional reference risk curve was derived via statistical analysis of the tests involving fractures in a recently reported dataset (Cormier et al., 2011a). Therein, Cormier et al., authors reported results for anterior-to-posterior dynamic loading of the frontal bone of rigidly supported heads of male post mortem human subjects, and fracture forces were measured in 22 cases. In Step 2, the FHBM head was used to conduct some underlying model validations relative to the Cormier tests. The model-based Force-at-Peak Stress was found to approximate the test-based Fracture Force.

Lower-body injury data for adults in real-world frontal impacts in the National Automotive Sampling System (NASS) were collected, analyzed, and modeled via statistical methods. Two levels of lower-body injury were considered: maximum serious-to-fatal (MAIS3+) and moderate-to-fatal (MAIS2+). In the analysis, we observed that a substantial fraction of all lower-body injured occupants had no recorded floor/toe pan intrusion: 47% of all MAIS3+ injured occupants; 69% of all MAIS2+ injured occupants. In the statistical modeling, we developed binary logistic regression models to fit the MAIS3+ and MAIS 2+ injury data. The statistically significant variables (p ≤ 0.05) were the speed change of the crash, postcrash floor/toe pan intrusion, level of restraint, occupant age, and occupant gender.

In the 1999 Supplemental Notice for Proposed Rulemaking (SNPRM) for Advanced Airbags, the National Highway Traffic Safety Administration (NHTSA) sought comments on the maximum speed at which the high-speed, unbelted occupant test suite will be conducted, i.e., 48 kph vs. 40 kph. To help address this question, an analysis of constraints was performed via extensive mathematical modeling of a theoretical restraint system. First, math models (correlated with several existing physical tests) were used to predict the occupant responses associated with 336 different theoretical dual-stage driver airbag designs subjected to six specific Regulated and non-Regulated tests.

Locations of key body segments of Hybrid III dummies used in FMVSS 208 compliance tests and NCAP tests were measured and subjected to statistical analysis. Mean clearance dimensions and their standard deviations for selected body segments of driver and passenger occupants with respect to selected vehicle surfaces were determined for several classes of vehicles. These occupant locations were then investigated for correlation with impact responses measured in crash tests and by using a three dimensional human-dummy mathematical model in comparable settings. Based on these data, the importance of some of the clearance dimensions between the dummy and the vehicle surfaces was determined. The study also compares observed Hybrid III dummy positions within selected vehicles with real world occupant positions reported in published literature.