Evaluation of Coastdown Analysis Techniques to Determine Aerodynamic Drag of Heavy-Duty Vehicles
Bibliographic record
Abstract
To investigate the feasibility of various aerodynamic test procedures for the Phase 2 Greenhouse Gas (GHG) Regulations for heavy-duty vehicles in the United States, the US Environmental Protection Agency conducted, through Southwest Research Institute (SwRI), coastdown testing of several heavy-duty tractors matched to a conventional 53-foot dry-van trailer. Three vehicle configurations were tested, two of which included common trailer drag-reduction technologies. Air speed was measured onboard the vehicle, and wind conditions were measured using a weather station placed along the road side. Tests were performed on a rural road in Texas. One vehicle configuration was tested over several days to evaluate day-to-day repeatability and the influence of changing wind conditions. Data on external sources of road forces, such as grade and speed dependence of tire rolling resistance, were collected separately and incorporated into the analysis. Various statistical and mathematical techniques are discussed in relation to challenges associated with using coastdown data to determine aerodynamic drag, including uncertainty, yaw angle determination, and asymmetry due to direction of travel.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".