Development of the CAVEMAN Human Body Model: Validation of Lower Extremity Sub-Injurious Response to Vertical Accelerative Loading

2017-22-0007

11/13/2017

Features
Event
61st Stapp Car Crash Conference
Authors Abstract
Content
Improving injury prediction accuracy and fidelity for mounted Warfighters has become an area of focus for the U.S. military in response to improvised explosive device (IED) use in both Iraq and Afghanistan. Although the Hybrid III anthropomorphic test device (ATD) has historically been used for crew injury analysis, it is only capable of predicting a few select skeletal injuries. The Computational Anthropomorphic Virtual Experiment Man (CAVEMAN) human body model is being developed to expand the injury analysis capability to both skeletal and soft tissues. The CAVEMAN model is built upon the Zygote 50th percentile male human CAD model and uses a finite element modeling approach developed for high performance computing (HPC). The lower extremity subset of the CAVEMAN human body model presented herein includes: 28 bones, 26 muscles, 40 ligaments, fascia, cartilage and skin. Sensitivity studies have been conducted with the CAVEMAN lower extremity model to determine the structures critical for load transmission through the leg in the underbody blast (UBB) environment. An evaluation of the CAVEMAN lower extremity biofidelity was also carried out using 14 unique data sets derived by the Warrior Injury Assessment Manikin (WIAMan) program cadaveric lower leg testing. Extension of the CAVEMAN lower extremity model into anatomical tissue failure will provide additional injury prediction capabilities, beyond what is currently achievable using ATDs, to improve occupant survivability analyses within military vehicles.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-22-0007
Pages
35
Citation
Butz, K., Spurlock, C., Roy, R., Bell, C. et al., "Development of the CAVEMAN Human Body Model: Validation of Lower Extremity Sub-Injurious Response to Vertical Accelerative Loading," SAE Technical Paper 2017-22-0007, 2017, https://doi.org/10.4271/2017-22-0007.
Additional Details
Publisher
Published
Nov 13, 2017
Product Code
2017-22-0007
Content Type
Technical Paper
Language
English