Mechanical-Thermal Simulation of Passenger-Loaded Vehicle Seat in Severe Winter Conditions
Bibliographic record
Abstract
This paper presents a comprehensive steady-state numerical study for an occupant-loaded vehicle seat with internal heating under severe winter conditions. A participant-based postural study showed that the nominal peak occupant seat pressure was 6kPa on the seat cushion, and 2.5kPa on the backrest. Uni-axial compression tests also indicated non-linear stress-strain behaviors in seating. Using an internally developed 3-D numerical model, it was found that the thermal resistance from contact and clothing was uniform (hc=144W·K−1·m−2) throughout the occupied regions. Their contribution to the overall thermal resistance was relatively minor, however, compared to that of skin (hoverall=27.2W·K−1·m−2). The thermal-mechanical simulations were conducted at heat input levels between 20W and 80W, using I-DEAS 10 and the TMG package as the simulation platform. Comparisons was also made between occupied seat with deflected and non-deflected mesh. The occupant deflection helped reduce the thermal resistance in the occupied region, which resulted in a slight decrease in heater temperature, accompanied by an increase in the average surface temperature, compared to corresponding undeformed results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".