Application of Energy-Absorbing Foam to the Seat Base to Mitigate Whiplash Injuries in Rear End Collisions
Bibliographic record
Abstract
Rear end collisions account for approximately $9 billion annually in the United States alone. These types of collisions account for nearly 30% of all vehicle impacts making them the most common type. Soft tissue injury to the neck (i.e. “whiplash”) is typically associated with this type of collision due to the occupant dynamics of the passengers in the struck vehicle. At low relative impact velocities, whiplash-type injuries are known to occur but are typically attributed to: 1) improper seat adjustment, 2) an “out-of-position” event, or 3) a low injury threshold due to age, gender, etc. In high impact collisions, both whiplash and occupant ejection can take place, the latter placing far greater risk of injury not only to the front seat occupant, but also to any rear seat passengers as well. The automobile seating system is the predominant safety device employed to protect the occupant during these types of collisions. In current seats, designers have focused predominantly on modifying seat base strength, seat back stiffness/compliance and head restraint size and position as a means of mitigating injury. While all of these aspects are important and have increased the crashworthiness of the seat, the concept of utilizing the seat base as a component to absorb collision energy has remained relatively unexamined, and may offer the potential for reducing injuries in higher severity collisions. This paper reports work related to the development of a supplemental safety system for the automobile seat where the goal is to further reduce the dynamic loading of the occupant to mitigate injury during a rear end collision. The initial step of this work was to develop and utilize several simulation models (using LS DYNA) to examine the relationship between collision conditions (such as vehicle-to-vehicle collision speeds, initial occupant and seat position, and occupant size) to accepted neck injury criteria for both low and high speed collisions. Utilizing these results, this paper examines the potential for deformable materials (in this study closed-cell foam) to absorb enough transmitted energy in order to reduce the dynamic loading experienced by the occupant. The relationships between energy levels transmitted to the occupant, the occupant dynamics, and how they affect the neck injury criteria assessed values are reported. Several foams, used industry wide, have been investigated in order to determine the best foam characteristics and geometrical shapes for low, high, and severe speed collisions. The results indicate that the foam's stress vs. volumetric strain curve, length, and cross-sectional area can affect the degree to which injury potential is reduced over a broad range of collision speeds. Further discussion of design considerations for such a supplemental system is also provided in this paper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".