Numerical Investigation on the Effect of Lateral Skirts Device on the Flow Dynamics around a Semi-trailer Truck
Bibliographic record
Abstract
Society of automative engineers (SAE) guidelines for computational fluid dynamics (CFD) and wind-tunnel tests on semi-trailer trucks were complied with to investigate the influence of adding a lateral skirts device—in the lower trailer part—on the improvement of the total drag force and the airflow structure around the truck. A reduced-scale (1:28) semi-trailer truck moving at three various speeds (i.e., 50 km/h, 75 km/h, and 100 km/h) is considered in this study. A reasonable agreement between experimental and numerical results was achieved in terms of the drag force parameter with a highest relative error of about 13% obtained in the case of the lowest speed (i.e., 50 km/h) of a truck without skirts. The numerical results yielded an average drag coefficient value of 0.48, which is reduced to 0.45 when the skirt device is added to the vehicle model. The airflow field analysis showed that the skirt device isolates and channels the flow toward the back in the lower part of the trailer, thus protecting the relatively structured flow from lateral disturbances that induce high turbulence which, in turn, generates an increase in the aerodynamic drag force.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".