Use of Rigid and Deformable Child Restraint Seats in Finite Element Simulations of Frontal Crashes
Bibliographic record
Abstract
This research focuses on the injury potential of children seated in forward facing child restraint seats during frontal vehicle crashes. Experimental sled tests were completed in accordance to the Federal Motor Vehicle Safety Standard 213 using a Hybrid III three-year-old dummy in a five point child restraint system. A full vehicle crash test was completed in accordance to the Canadian Motor Vehicle Safety Standard 208 with the addition of a three-year-old Hybrid III crash test dummy, seated behind the passenger seat, restrained in the identical five-point child safety seat. Different child restraint finite element models were developed incorporating a subset of the apparatus used in the two experimental tests and simulated using LS-DYNA. Three different finite element models utilizing (i) an assumed rigid CRS seat model containing only pertinent surfaces which come into contact with the Hybrid III dummy, (ii) a completely deformable CRS model incorporating material nonlinearities determined through standardized tensile tests completed on specimens extracted from various portions and parts of the CRS with complete use of CAD data for the spatial discretization, and (iii) a rigid model implementation of the deformable seat incorporating material nonlinearities of the seat belt webbing. Observations from the numerical simulations were compared to experimental values of the head and chest accelerations with a reasonable correlation observed. Values of the head injury criteria, calculated using a 15 ms window ranged from 50 to 100 for FMVSS 213 pulse, and 300 to 400 for CMVSS 208 pulse. For all three child restraint models, the child dummy exhibited substantially higher values for the neck injury criteria as compared to the experimental tests.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".