Bibliographic record
Abstract
Road-vehicle platooning is known to reduced aerodynamic drag. Recent aerodynamic-platooning investigations have suggested that follower-vehicle drag-reduction benefits persist to large, safe inter-vehicle driving distances experienced in everyday traffic. To investigate these traffic-wake effects, a wind-tunnel wake-generator system was designed and used for aerodynamic-performance testing with light-duty-vehicle (LDV) and heavy-duty-vehicle (HDV) models. This paper summarizes the development of this Road Traffic and Turbulence System (RT2S), including the identification of typical traffic-spacing conditions, and documents initial results from its use with road-vehicle models. Analysis of highway-traffic-volume data revealed that, in an uncongested urban-highway environment, the most-likely condition is a speed of 105 km/h with an inter-vehicle spacing of about 50 m. Probability distributions for spacing and road speed were used to identify a range of suitable inter-vehicle spacings to target for wake conditions. Combining these data with previous research activities that examined the characteristics of road-vehicle wakes, three phases of development for the RT2S were undertaken in multiple wind tunnels leading to a system using porous grids and sets of vertically-oriented vanes. Specific grid and vane combinations generate wake shapes, wind-speed deficits, flow-angularities, and turbulence representative of every-day traffic wakes. Lateral positioning of the system and rotation of the vanes provide wake positioning and flow characteristics representing a variety of wake-in-crosswind conditions, while being able to effectively change the lane of the wake-source vehicles. The results of two experiments are presented to document the influence of traffic wakes, via application of the RT2S, on the aerodynamic performance of road vehicles. First, measurements are presented based on the use of a prototype version of the system with a 15%-scale DrivAer fastback model. Drag reductions from 10% to 31% and side-force-coefficient reductions in excess of 50% were observed for the DrivAer model, relative to uniform-flow conditions, for the 13 specific wake-like conditions replicated. The second set of experiments applied the final RT2S design to testing of a 30%-scale tractor-trailer HDV model, which showed drag reductions as high as 15% for an HDV-wake configuration, with drag reductions of 2% measured for a compact-sedan-wake at 50 m effective forward distance, relative to uniform winds. For both sets of experiments, examining wake effects on LDV and HDV models, changes in aerodynamic performance are attributed in large part to reductions in effective dynamic pressure, but surface-pressure measurements indicate that flow-angularity variations also play a role in crosswind conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".