A Semi-Automated Approach to Real World Motor Vehicle Crash Reconstruction Using a Generic Simplified Vehicle Buck Model

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. A Latin hypercube sample space (9 variables) of 120 simulations was created to vary occupant safety and restraint properties. Sprague and Geers magnitude and phase error factors were used to identify an optimal set of restraint parameters to reconstruct the HIII kinematic and kinetic responses. Using the tuned SVM, event data recorder pulses from real world crashes, and the Total HUman Model for Safety, LS-Dyna simulations were used to reconstruct the occupant-vehicle interactions. In a sample case, stress, strain, and dynamic loads were evaluated to predict rib, sternum, and vertebral injuries sustained by the occupant in the crash.