A methodology for improving structural robustness in frontal car-to-car crash scenarios
Bibliographic record
Abstract
There has been significant development in passenger car crashworthiness over the last few decades. However, real-world crashes often occur in scenarios dissimilar to laboratory barrier crash set-ups. Further knowledge is required on how different impact scenarios affect vehicle structural response and occupant injury risk in real-world scenarios. This study introduces a methodology for assessing crash configuration parameters that influence the structural response in car-to-car frontal collisions by using finite element models of two identical vehicles. The crash configuration parameters included in this study were initial velocities, oblique angle and lateral offset distance. An evaluation was made in terms of passenger compartment intrusion and crash pulse severity. Special focus was directed towards investigating whether these input parameters can be used to define incompatible scenarios, i.e. where the structural response in one vehicle is significantly different compared to the other vehicle. Results indicate that collision scenarios with large overlap as extreme in terms of crash pulse severity, and incompatible car-to-car crash scenarios were found at small overlap and an oblique angle of 15°. An outlook for future model and method validation work is described.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".