Wind-Load and Surface-Pressure Measurements of the Aerodynamic Interactions of a Passenger Vehicle with Adjacent-Lane Vehicles
Bibliographic record
Abstract
The mutual aerodynamic influence of road vehicles in close proximity is known to alter significantly the drag performance of the vehicles. This paper presents an extended analysis from a study of two open-access road-vehicle shapes (a DrivAer Notchback model and an AeroSUV Estateback model) in close lateral proximity with each other, or with other vehicle shapes. Wind-tunnel measurements were conducted for a yaw-angle range of ±10°, for 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. The results of a previous analysis of the data, which examined aerodynamic force measurements only, showed changes in drag coefficient of ±20% or more depending on the relative locations and wind conditions. In this paper, the force-coefficient results reexamined, and surface-pressure measurements are introduced to investigate the sources of the performance changes. The results suggest that the changes in aerodynamic performance of vehicles in close lateral proximity arise from three mechanisms: 1) from a combination of locally-reduced static pressure, due to the combined blockage effect of the two vehicles on the local flow field; 2) from local flow-angularity changes altering the effective yaw angle of each vehicle; and 3) from wake-body interactions. The results also demonstrate a small increase in proximity effect when a proximate Ahmed body is introduced instead, likely as a results of its larger internal volume, but with trends that match the DrivAer/AeroSUV results. Proximity-induced loads and pressures are shown to increase with reduced distance and with increased adjacent-body size.
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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 | 0.001 | 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".