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Validation of a Finite Element 50th Percentile THOR Anthropomorphic Test Device in Multiple Sled Test Configurations
Published November 12, 2018 by The Stapp Association in United States
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Computational models of anthropomorphic test devices (ATDs) can be used in crash simulations to quantify the injury risks to occupants in both a cost-effective and time-sensitive manner. The purpose of this study was to validate the performance of a 50th percentile THOR finite element (FE) model against a physical THOR ATD in 11 unique loading scenarios. Physical tests used for validation were performed on a Horizontal Impact Accelerator (HIA) where the peak sled acceleration ranged from 8-20 G and the time to peak acceleration ranged from 40-110 ms. The directions of sled acceleration relative to the THOR model consisted of -Gx (frontal impact), +GY (left-sided lateral impact), and +GZ (downward vertical impact) orientations. Simulation responses were compared to physical tests using the CORrelation and Analysis (CORA) method. Using a weighted method, the average response and standard error by direction was +Gy (0.83±0.03), -Gx (0.80±0.01), and +Gz (0.76±0.03). Qualitative and quantitative results demonstrated the FE model’s kinetics and kinematics were sufficiently validated against its counterpart physical model in the tested loading directions.
- Kyle P. McNamara - Wake Forest School of Medicine, Virginia Tech - Wake Forest
- Derek A. Jones - Wake Forest School of Medicine, Virginia Tech - Wake Forest
- James P. Gaewsky - Wake Forest School of Medicine, Virginia Tech - Wake Forest
- Jacob B. Putnam - KBRwyle
- Jeffrey T. Somers - KBRwyle
- Ashley A. Weaver - Wake Forest School of Medicine, Virginia Tech - Wake Forest
- Joel D. Stitzel - Wake Forest School of Medicine, Virginia Tech - Wake Forest
CitationMcNamara, K., Jones, D., Gaewsky, J., Putnam, J. et al., "Validation of a Finite Element 50th Percentile THOR Anthropomorphic Test Device in Multiple Sled Test Configurations," SAE Technical Paper 2018-22-0012, 2018, https://doi.org/10.4271/2018-22-0012.
Data Sets - Support Documents
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