Seat Structural Design Choices and the Effect on Occupant Injury Potential in Rear End Collisions
Bibliographic record
Abstract
The seat is the most important safety device available to vehicle occupants during rear end collisions, and thus proper design and structural integrity of the seat under expected impact loading is essential. The objective of the current research work is to increase our understanding of the design requirements for seat performance in relation to injury producing collisions, and to examine how various seat design parameters affect both structural integrity and occupant protection. A numerical model-based parametric study was developed based upon the 2002 GM Grand Am seat. The parametric study utilizes a 50th percentile male dummy, applies the FMVSS 202 standard crash pulse to selected structural variations of this seat, and then utilizes the neck injury criterion (NIC) and neck displacement criterion (NDC) to assess the likelihood of injury. The recliner rotational stiffness and the head restraint stiffness are the most significant seat structural design choices that can mitigate injury potential. Trends indicate that dual recliners offer reduced injury potential over single recliner seat designs. Injury reduction is seen through a compliant thoracic seatback region. Conversely, allowing pelvic translation increases the likelihood of injury through increased seat and occupant differential velocities. The results indicate that the risk of occupant ejection is reduced significantly through the use of seat-mounted seatbelt retractors, although dual retractor systems mounted to the b-pillar and seat base provide comparable ramping reduction.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".