Numerical Analysis of Lateral Skirts Performance on Drag Force of a Semi-Trailer Truck
Bibliographic record
Abstract
This work performs computational fluid dynamics (CFD) simulations using a transient URANS (unsteady Reynolds averaged Navier–Stokes) turbulence model to investigate the influence of lateral skirts — located in the lower part of a semitrailer truck — in terms of reducing the total drag force and fuel consumption savings. The total drag force values are calculated for three semi-trailer trucks speeds (i.e. 60, 70 and 100 km/h), compared, and then validated against experimental results carried out in a wind tunnel reduced model scale (1:28). The relative errors of the aerodynamic drag force parameter are assessed in order to quantify the accuracy and the reliability of the numerical modeling results with regard to the experimental results. In addition, the flow pattern around the semi-trailer truck is then investigated to determine how the induced flow field is channeled, and where the recirculating zones are modified and developed when using the additional skirt device.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".