A Comparison of the Kinematics of a Child Finite Element Model and the HYBRID III 3-Year-Old Dummies in Frontal Crashes
Bibliographic record
Abstract
The THUMS (Total HUman Model for Safety) 3-year-old child finite element (FE) model was developed by Toyota Central R&D Labs (TCRDL) by model-based scaling from the AM50 (50 percentile male) human FE model. The objective of this paper is to present a comparison between the kinematics of a child FE model developed from the adult THUMS model and a HYRID III 3-year-old child dummy using observations from numerical simulations of a CMVSS 208 frontal crash. Both the child models were positioned in a forward facing, five point child restraint systems (CRS). An acceleration pulse acquired from a vehicle crash test in accordance with Canadian Motor Vehicle Safety Standards (CMVSS) 208 was applied to the seat buck supporting the CRS. Numerical simulations with both the child model and the Hybrid III child dummy were conducted using LS-DYNA version 970. Through a quantitative analysis of the head accelerations, it was observed that the 3-year-old child model experienced greater magnitudes of head accelerations in the global x-direction as compared to the Hybrid III three-year-old child dummy. In addition, the child model exhibited an increased flexibility of the neck and a greater degree of deformation in the thorax. The child model exhibited an 18% greater magnitude of head rotation about the y-axis compared to the Hybrid III child dummy. The head injury criteria values calculated over a 36 ms window were observed to be 480 for the child model and 576 for the Hybrid III child dummy respectively. By comparing the head trajectories of the two FE models with an experimentally completed crash test using a child cadaver it was observed that the child model is more capable of predicting the kinematics believed to be experienced by a child body. The results of this study may act as a reference to aid in improving the accuracy of numerical simulations for child safety in vehicle crashes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".