Track-Based Aerodynamic Testing of a Heavy-Duty Vehicle: Coast-Down Measurements
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In an effort to support Phase 2 of Greenhouse Gas Regulations for Heavy-Duty Vehicles in the United States, a track-based test program was jointly supported by Transport Canada (TC), Environment and Climate Change Canada (ECCC), the U.S. Environmental Protection Agency (EPA), and the National Research Council Canada (NRC) to assess aerodynamic evaluation methodologies proposed by the EPA and to provide a site-verification exercise against a previous test campaign with the same vehicle. Coast-down tests were conducted with a modern aerodynamic tractor matched to a conventional 16.2 m (53 ft) dry-van trailer, and outfitted with two drag reduction technologies. Enhanced wind-measurement instrumentation was introduced, consisting of a vehicle-mounted fast-response pressure probe and track-side sonic anemometers that, when used in combination, provided improved reliability for the measurements of wind conditions experienced by the vehicle.</div><div class="htmlview paragraph">Results from the coast-down tests compare well when analyzed using two different techniques: a conventional regression method, and the recently-proposed high-low iteration method. Drag-area results from the the coast-down tests for two vehicle configurations, analyzed with the EPA-proposed high/low iteration method, differ by no more than 3% from the EPA-derived values for a low-yaw range of ±3°yaw. Several modelling assumptions were included in the analysis to account for environmental factors and vehicle-component performance, such as the mechanical drive-line losses, the rolling-resistance speed dependence, and the winds experienced by the vehicle. These models are shown to improve the fidelity of the coast-down analysis, but require further validation for their real-world applicability. Additional considerations for the placement of on-board wind anemometry for improved accuracy of results are discussed.</div></div>
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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 | 0.000 |
| 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