Author
Listed:
- Mitesh Lalwala
- Bharath Koya
- Karan Devane
- F. Scott Gayzik
- Ashley A. Weaver
Abstract
Spinal injuries are a concern for automotive applications, requiring large parametric studies to understand spinal injury mechanisms under complex loading conditions. Finite element computational human body models (e.g. Global Human Body Models Consortium (GHBMC) models) can be used to identify spinal injury mechanisms. However, the existing GHBMC detailed models (with high computational time) or GHBMC simplified models (lacking vertebral fracture prediction capabilities) are not ideal for studying spinal injury mechanisms in large parametric studies. To overcome these limitations, a modular 50th percentile male simplified occupant model combining advantages of both the simplified and detailed models, M50-OS + DeformSpine, was developed by incorporating the deformable spine and 3D neck musculature from the detailed GHBMC model M50-O (v6.0) into the simplified GHBMC model M50-OS (v2.3). This new modular model was validated against post-mortem human subject test data in four rigid hub impactor tests and two frontal impact sled tests. The M50-OS + DeformSpine model showed good agreement with experimental test data with an average CORrelation and Analysis (CORA) score of 0.82 for the hub impact tests and 0.75 for the sled impact tests. CORA scores were statistically similar overall between the M50-OS + DeformSpine (0.79 ± 0.11), M50-OS (0.79 ± 0.11), and M50-O (0.82 ± 0.11) models (p > 0.05). This new model is computationally 6 times faster than the detailed M50-O model, with added spinal injury prediction capabilities over the simplified M50-OS model.
Suggested Citation
Mitesh Lalwala & Bharath Koya & Karan Devane & F. Scott Gayzik & Ashley A. Weaver, 2024.
"Modular incorporation of deformable spine and 3D neck musculature into a simplified human body finite element model,"
Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 27(1), pages 45-55, January.
Handle:
RePEc:taf:gcmbxx:v:27:y:2024:i:1:p:45-55
DOI: 10.1080/10255842.2023.2168537
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