Aerodynamic Drag of Road Vehicles in Close Lateral Proximity
Bibliographic record
Abstract
Aerodynamic interaction between vehicles on a roadway can modify the fuel use and greenhouse gas emissions of the vehicle relative to their performance under isolated, uniform-wind conditions. A comprehensive wind-tunnel study was undertaken to examine changes to the aerodynamic drag experienced by vehicles in close proximity, in adjacent lanes. Wind-load measurements were conducted for two general configurations: 15%-scale testing with light-duty-vehicle (LDV) models, and 6.7%-scale testing with a heavy-duty vehicle (HDV) model. For the LDV study, a DrivAer model was tested with a proximate AeroSUV model or an Ahmed model at lateral distances representing 75%, 100%, and 125% of a typical highway lane spacing, and for longitudinal distances up to 2 vehicle lengths forward and back. Commensurate measurements were conducted for the AeroSUV model with the proximate DrivAer or Ahmed model. For the HDV study, a tractor-trailer model was tested with a single or combination of adjacent-lane models. Measurements were conducted over a range of yaw angles, permitting wind-averaged-drag evaluations. Changes in drag coefficient on the order of 25% or more were observed, relative to isolated-vehicle conditions. Adjacent-lane proximity-vehicle effects can increase or decrease the drag of a vehicle, with the strongest effects being an increase when vehicle models are side-by-side or when an HDV has its aft end coincident with another vehicle. The LDV-model pairing demonstrated that, for a two-vehicle arrangement, the increased drag on one vehicle can be offset by the decrease in drag on the other, and that proximity-vehicle effects should be examined in the context of the overall vehicle-system performance.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".