Influence Mechanism of a Bridge Wind Barrier on the Stability of a Van-Body Truck under Crosswind
Bibliographic record
Abstract
Understanding the crosswind stability of cars under strong wind loads and research on wind resistance methods is important for improving the safety performance of wind-induced driving on bridges. Taking van-body trucks as the research object, numerical calculation methods and wind tunnel test methods are used to conduct the wind-induced driving safety analyses of van trucks on a cross-sea bridge. The influence of the structural parameters of the barrier-type wind barrier on the aerodynamic characteristics and straight-line driving stability of the trucks on the bridge is studied and analyzed quantitatively. The results show that the decrease in the porosity of the wind barrier can effectively reduce the average wind speed of the bridge deck, and increasing the height of the wind barrier can effectively reduce the wind speed and increase the occlusion height of the bridge deck. The lateral acceleration, yaw rate, and lateral displacement of trucks decrease with the decrease in the porosity of the wind barrier and decrease with the increase in the height of the wind barrier. The research conclusions can not only provide data support for wind-induced driving safety analysis and the wind-resistant design of bridges but also provide a new method to balance the requirements of bridge wind-induced driving safety and bridge wind-induced structure safety.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".