Vehicle crash reconstructions using FE human body model to improve injury predictions

Abstract

FE Human Body Models (HBMs) represent an important researching tool for assessing occupant injuries on tissue level. Although prior research has overtime led to considerable improvements, further validation of today’s HBMs is needed in order to ensure model biofidelity. In this thesis, SAFER HBM v9 is validated through reconstructions of 11 real world motor vehicle crashes with known injury outcomes. The SAFER HBM is positioned into a generic simplified vehicle model (SVM) by using Marionette method. The SAFER HBM is then scaled in four of the reconstruction cases in order to better match occupant statures. After the model pre-set in ANSA, simulations are run in the FE- solver LS-DYNA and the obtained results are analyzed in the LS-PrePost and META. The injury validation is based on comparing simulation results with the reported real-world injury outcomes of head, ribcage and lumbar spine for the selected reconstruction cases. In particular, AIS2+ risk for concussion is estimated considering 1st Principal Green St Venant strain in corpus callosum, gray matter and white matter of the KTH brain model. Moreover, AIS2+ probability fracture risk is estimated for the entire ribcage based on strain-based probabilistic method with age adjustment. In addition, forces and moments are analyzed in lumbar spine vertebrae and are related to compression fracture injuries based on existing thresholds suggested in previous studies. The concussion results show that the model significantly underestimates the probability for concussion for the cases with reported severe head injuries. The preliminary conclusion from the evaluated risk for rib fractures is that the model in average overpredicts the AIS2+ probability risk. The applied lumbar criterion shows a progressively increased fracture probability risk from vertebra L1 to L5, regardless of the real-world fracture outcome. When considering lumbar compression forces independent to other loads, the cases where lumbar fractures where reported indicates on average higher compression forces in comparison to non-fracture cases. Finally, improvements are suggested for the implemented validation methods, in order to increase reliability for the future injury predictions.