Effect of body size and enhanced helmet systems on risk for motorsport drivers.

OBJECTIVE: Computational modeling has been shown to be a useful tool for simulating representative motorsport impacts and analyzing data for relative injury risk assessment. Previous studies have used computational modeling to analyze the probability of injury in specific regions of a 50th percentile male driver. However, NASCAR drivers can represent a large range in terms of size and female drivers are becoming increasingly more common in the sport. Additionally, motorsport helmets can be outfitted with external attachments, or enhanced helmet systems (EHS), whose effect is unknown relative to head and neck kinematics. The current study expands on this previous work by incorporating the F05-OS and M95-OS into the motorsport environment in order to determine correlations between metrics and factors such as PDOF, resultant ΔV occupant size, and EHS. METHODS: A multi-step computational process was used to integrate the Global Human Body Models Consortium family of simplified occupant models into a motorsport environment. This family included the 5th percentile female (F05-OS), 50th percentile male (M50-OS), and 95th percentile male (M95-OS), which provide a representative range for the size and sex of drivers seen in NASCAR's racing series'. A series of 45 representative impacts, developed from real-world crash data, and set of observed on-track severe impacts were conducted with these models. These impacts were run in triplicate for three helmet configurations: bare helmet, helmet with visor, helmet with visor and camera. This resulted in 450 total simulations. A paired t-test was initially performed as an exploratory analysis to study the effect of helmet configuration on 10 head and neck injury metrics. A mixed-effects model with unstructured covariance matrix was then utilized to correlate the effect between five independent variables (resultant ΔV, body size, helmet configuration, impact quadrant, and steering wheel position) and a selection of 25 metrics. All simulations were conducted in LS-Dyna R. 9.1. RESULTS: Risk estimates from the M50-OS with bare helmet were used as reference values to determine the effect of body size and helmet configuration. The paired t-test found significance for helmet configuration in select head-neck metrics, but the relative increase in these metrics was low and not likely to increase injury risk. The mixed-effects model analyzed statistical relationships across multiple types of variables. Within the mixed-effects model, no significance was found between helmet configuration and metrics. The greatest effect was found from resultant ΔV, body size, and impact quadrant. CONCLUSIONS: Overall, smaller drivers showed statistically significant reductions in injury metrics, while larger drivers showed statistically significant increases. Lateral impacts showed the greatest effect on neck metrics and, on average, showed decreases for head metrics related to linear acceleration and increases for head metrics related to angular velocity. HBM parametric studies such as this may provide an avenue to assist injury detection for motorsport incidents, improve triage effectiveness, and assist in the development of safety standards.

Simulation-based assessment of injury risk for an average male motorsport driver.

OBJECTIVE: While well-protected through a variety of safety countermeasures, motorsports drivers can be exposed to a large variety of crash modes and severities. Computational human body models (HBMs) are currently used to assess occupant safety for the general driving public in production vehicles. The purpose of this study was to incorporate a HBM into a motorsport environment using a simulation-based approach and provide quantitative data on relative risk for on-track motorsport crashes. METHODS: Unlike a traditional automotive seat, the NASCAR driver environment is driver-customized and form-fitting. A multi-step process was developed to integrate the Global Human Body Models Consortium (GHBMC) 50th percentile male simplified occupant into a representative motorsport environment which includes a donned helmet, a 7-point safety belt system, head and neck restraint (HNR), poured-foam seat, steering wheel, and leg enclosure. A series of 45 representative impacts, developed from real-world crash data, of varying severity (10 kph ≤ ΔV ≤ 100 kph) and impact direction (∼290° ≤ PDOF ≤ 20°) were conducted with the GHBMC 50th percentile male simplified occupant (M50-OS v2.2). Kinematic and kinetic data, and a variety of injury criteria, were output from each of the simulations and used to calculate AIS 1+, 2+, and 3+ injury risk. All simulations were conducted in LS-Dyna R. 9.1. RESULTS: Injury risk of the occupant using the previously mentioned injury criteria was calculated for the head, neck, thorax, and lower extremity, and the probability of injury for each region was plotted. Of the simulated impacts, five had a maximum AIS 1+ injury risk >20%, six had a maximum AIS 2+ injury risk >10%, and no cases had a maximum AIS 3+ injury >1%. Overall, injury risk estimates were reasonable compared to on-track data reported from Patalak et al. (2020). CONCLUSIONS: Beyond injury risk, the study is the first of its kind to provide mechanical loading values likely experienced during motorsports crash incidents with crash pulses developed from real-world data. Given the severity of the crash pulses, the simulated environments reinforce the need for the robust safety environment implemented by NASCAR.

Development and Multi-Scale Validation of a Finite Element Football Helmet Model.

Head injury is a growing concern within contact sports, including American football. Computational tools such as finite element (FE) models provide an avenue for researchers to study, and potentially optimize safety tools, such as helmets. The goal of this study was to develop an accurate representative helmet model that could be used in further study of head injury to mitigate the toll of concussions in contact sports. An FE model of a Schutt Air XP Pro football helmet was developed through three major steps: geometry development, material characterization, and model validation. The fully assembled helmet model was fit onto a Hybrid III dummy head-neck model and National Operating Committee on Standards for Athletic Equipment (NOCSAE) head model and validated through a series of 67 representative impacts similar to those experienced by a football player. The kinematic and kinetic response of the model was compared to the response of the physical experiments, which included force, head linear acceleration, head angular velocity, and carriage acceleration. The outputs between the model and the physical tests were quantitatively evaluated using CORelation and Analysis (CORA), amounting to an overall averaged score of 0.76. The model described in this study has been extensively validated and can function as a building block for innovation in player safety.

Correction to: Development and Multi-Scale Validation of a Finite Element Football Helmet Model.

This correction is to add the middle initials of all authors, which were inadvertently omitted from the original article.