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Computational finite element (FE) modeling of real world motor vehicle crashes (MVCs) is valuable for analyzing crash-induced injury patterns and mechanisms. Due to unavailability of detailed modern FE vehicle models, a simplified vehicle model (SVM) based on laser scans of fourteen modern vehicle interiors was used. A crash reconstruction algorithm was developed to semi-automatically tune the properties of the SVM to a particular vehicle make and model, and subsequently reconstruct a real world MVC using the tuned SVM. The required algorithm inputs are anthropomorphic test device position data, deceleration crash pulses from a specific New Car Assessment Program (NCAP) crash test, and vehicle interior property ranges. A series of automated geometric transformations and five LSDyna positioning simulations were performed to match the FE Hybrid III’s (HIII) position within the SVM to reported data. Once positioned, a baseline simulation using the crash test pulse was created.

Crash reconstructions using finite element (FE) vehicle and human body models (HBMs) allow researchers to investigate injury mechanisms, predict injury risk, and evaluate the effectiveness of injury mitigation systems, ultimately leading to a reduced risk of fatal and severe injury in motor vehicle crashes (MVCs). To predict injuries, regional-level injury metrics were implemented into the Total Human Model for Safety (THUMS) full body HBM. THUMS was virtually instrumented with cross-sectional planes to measure forces and moments in the femurs, upper and lower tibias, ankles, pelvis (pubic symphysis, ilium, ischium, sacrum, ischial tuberosity, and inferior and superior pubic ramus), and the cervical, thoracic, and lumbar vertebrae and intervertebral discs. To measure accelerations, virtual accelerometers were implemented in the head, thoracic vertebrae, sternum, ribs, and pelvis. Three chest bands and an abdominal band were implemented to measure chest and abdominal deflection.