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.
Keywords: GHBMC; Motorsport injury risk; computational human body modeling; simplified occupant model.