An Investigation of the Influence of Close-Proximity Traffic on the Aerodynamic Drag Experienced by Tractor-Trailer Combinations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Recent research to investigate the aerodynamic-drag reduction associated with truck platooning systems has begun to reveal that surrounding traffic has a measurable impact on the aerodynamic performance of heavy trucks. A 1/15-scale wind-tunnel study was undertaken to measure changes to the aerodynamic drag experienced by heavy trucks in the presence of upstream traffic. The results, which are based on traffic conditions with up to 5 surrounding vehicles in a 2-lane configuration and consisting of 3 vehicle shapes (compact sedans, SUVs, and a medium-duty truck), show drag reductions of 1% to 16% for the heavy truck model, with the largest reductions of the same order as those experienced in a truck-platooning scenario. The data also reveal that the performance of drag-reduction technologies applied to the heavy-truck model (trailer side-skirts and a boat-tail) demonstrate different performance when applied to an isolated vehicle than to conditions with surrounding traffic. The results suggest that vehicle shape optimization strategies may differ if the influence of wake effects from surrounding traffic is included in product development cycles. Additionally, truck-platooning benefits should be taken in the context of typical traffic scenarios for which trucks are already experiencing a background-platooning effect and therefore may not be expected to attain the benefits relative to isolate-vehicle conditions.</div></div>
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 it