Effect of acceleration pulse shape on the safety of unbelted motorcoach passengers in frontal collision under uncertainty of their seating posture
Bibliographic record
Abstract
Crashworthiness design of motorcoaches is a new research area driven by the constantly upgrading crash safety regulations. One important parameter to be considered during the crashworthiness design is the shape of acceleration profile experienced by the vehicle’s occupants. The paper presents the results of the analysis of the influence of the pulse shape on the safety of motorcoach passengers in a frontal collision. Owing to a large number of uncertainties involved in a real-world crash, a probabilistic approach is undertaken for the parametric study. For each considered acceleration pulse, several random occupant postures are generated and used in the numerical sled test. The influence of the pulse shape on occupants’ injury criteria is then evaluated and compared to the amount of scatter introduced by the random postures. The results indicate the effect of the uncertainty in the seating posture overcomes the effect of the pulse shape changes. The coefficient of variation (CV) in the Head Injury Criterion (HIC) across stochastic postures is found to be from 11% to 34% while the average total change in the HIC values between different acceleration profiles is only 8%. Overall, the research demonstrates the significance of the consideration of the posture variation in the parametric crash test studies.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".