Influencing Factors and Sensitivity Analysis of Occupant Impact Injury in Passenger Compartment
Bibliographic record
Abstract
OBJECTIVE: The study reported in this article addressed the influence of each factor (impact acceleration, table height h, table to seat distance l₁, interseat distance l₂, table contact stiffness k₁, seat contact stiffness k₂, etc.) on the extent of occupant impact injury in a railway vehicle secondary collision. METHODS: The vehicle's passenger compartment-occupant coupling model was established using proprietary software (MADYMO). The simulated occupant was MADYMO's validated Hybrid III 50th percentile dummy model, and the vehicle's passenger compartment model included the floor surface, side wall, seat (with cushion), backrest, and table. The floor surface and side wall were modeled as flat surfaces; the seat (with its cushion), backrest, and table were modeled as ellipsoids. Some 25 samples were selected for numerical simulation based on a 2-factor, 5-level, full-factorial experimental design: the response surface method (RSM) was applied to fit the mapping relationship between the occupant's injury parameters (head injury criterion [HIC] and thoracic cumulative 3-ms injury criterion [TC(3ms)]) and other multi-influence factors. RESULTS: Taking the seat-table structure model and seat-seat structure model as examples, the influence of each factor on the extent of the passenger compartment occupant's impact injury was assessed from the basis of traditional passenger compartment configurations found on Chinese trains. The sensitivity analysis of occupant injury parameters on these influence factors was carried out to determine the extent of the influence of each factor on each impact injury parameter. CONCLUSIONS: The response surfaces of the occupant's injury parameters (HIC and TC(3ms)), and changes therein as the system's variables were altered showed that impact injury parameters and change thereto could be described intuitively and qualitatively. Some meaningful conclusions were obtained through the sensitivity analysis of occupant injury parameters to changes in these influence factors. The research offered a guideline for the design and manufacture of a train's passenger compartment structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".