Track-Based Aerodynamic Testing of a Two-Truck Platoon
Bibliographic record
Abstract
Fuel savings from truck platooning are generally attributed to an aerodynamic drag-reduction phenomena associated with close-proximity driving. The current paper is the third in a series of papers documenting track testing of a two-truck platoon with a Cooperative Adaptive Cruise Control (CACC) system where fuel savings and aerodynamics measurements were performed simultaneously. Constant-speed road-load measurements from instrumented driveshafts and on-board wind anemometry were combined with vehicle measurements to calculate the aerodynamic drag-area of the vehicles. The drag-area results are presented for each vehicle in the two-truck platoon, and the corresponding drag-area reductions are shown for a variety of conditions: gap separation distances (9 m to 87 m), lateral offsets (up to 1.3 m), dry-van and flatbed trailers, and in the presence of surrounding traffic. For the standard aligned platoon, the results demonstrate up to 8% drag reduction for the lead vehicle, with drag reductions exceeding 20% for the trailing vehicle at some yaw angles. Wind-velocity measurements on the following truck suggest that the drag-area reductions are due to a combined effect of reduced apparent wind speed and reduced effective yaw angle in the wake of the lead truck. In the presence of a three-vehicle traffic pattern forward of a single truck, drag-area reductions on the order of 10% were observed relative to the same truck travelling in isolation. When platooning with this surrounding-traffic pattern, the trends and magnitudes of aerodynamic drag reduction are shown to be retained, relative to the platoon in the absence of other traffic, corroborating observed trends in of fuel-savings performed simultaneously. As a supplement to the current study, a first-of-its-kind coast-down test was undertaken with the two-truck platoon where the CACC system was used to maintain a constant distance between the vehicles during each coast. The CACC system was used on the following vehicle when the lead vehicle was coasting and on the lead vehicle when the follower was coasting. Despite some scatter in the data from this proof-of-concept study, the results are consistent with those of the principal constant-speed measurement technique of this paper. This preliminary study demonstrates that the coast-down test method, which previously was only applied for single vehicles, is also applicable to vehicle platoons.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".