Study of Occupant Safety and Airbag Deployment Time
Bibliographic record
Abstract
This paper presents the use of Finite Element Analysis (FEA) software in recreating a full frontal barrier impact test with a 50th percentile male hybrid III dummy to investigate various passenger vehicle airbag deployment times for the development of an airbag trigger sensor. Results for the physical full frontal barrier impact test where prepared by MGA Research Corporation with a 2007 Toyota Yaris. Using a nonlinear transient dynamic FEA software, a virtual full frontal barrier impact test was created to reproduce the physical results and trends experienced in the physical crash test found in a report by the National Highway Traffic Safety Administration (NHTSA) 5677. The results of the simulation were compared to the results of the physical crash which produced similar trends, but not the same values. The simulation was then used in testing different passenger vehicle airbag deployment times to see its results on specific occupant injury criteria’s; Head Injury Criterion (HIC), Chest Compression Criterion (CC). Four different vehicle speeds where used; 20 km/h, 40 km/h, 56 km/h, and 90 km/h in conjunction with a range of +/− 6 milliseconds in the airbag deployment timing. Results of the airbag deployment timing showed that trends of faster airbag deployment times resulted in lower values for HIC and CC. Following these trends, suggestions for airbag deployment trigger distances were developed to aid in creation of an advanced airbag deployment sensor or crash sensor. While the simulation has yet to be validated, the trends may be assessed and actual values may differ.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".