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Automating Regional Rib Fracture Evaluation in the GHBMC Detailed Average Seated Male Occupant Model
Technical Paper
2017-01-1428
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Computational modeling of the human body is increasingly used to evaluate countermeasure performance during simulated vehicle crashes. Various injury criteria can be calculated from such models and these can either be correlative (HIC, BrIC, etc.) or based on local deformation and loading (strain-based rib fracture, organ damage, etc.). In this study, we present a method based on local deformation to extract failed rib region data. The GHMBC M50-O model was used in a Frontal-NCAP severity sled simulation. Failed Rib Regions (FRRs) in the M50-O model are handled through element deletion once the element surpasses 1.8% effective strain. The algorithm central to the methodology presented extracts FRR data and requires 4-element connectivity to register a failure. Furthermore, the FRRs are localized to anatomical sections (Lateral, Anterior, and Posterior), rib level (1,2,3 etc.) and element strain data is recorded. FRRs crossing multiple anatomical sections were treated in each section but can be back calculated using a total failure count. These were then used to generate visual summaries for each time point with FRRs per each rib level and section depicted numerically and visually through an overlaid heat map. A total failure count is also displayed at the bottom of each table for each side. While correlative solutions for rib injuries have been published, the methodology presented is for users who prefer to investigate rib failure through element elimination. The techniques employed here are similar to methods presented in literature to determine real-world rib fracture location and patterns. A sample case with a nominal delta-V of 56.4 kph was examined for algorithm evaluation.
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Authors
Citation
Guleyupoglu, B., Barnard, R., and Gayzik, F., "Automating Regional Rib Fracture Evaluation in the GHBMC Detailed Average Seated Male Occupant Model," SAE Technical Paper 2017-01-1428, 2017, https://doi.org/10.4271/2017-01-1428.Data Sets - Support Documents
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