Measurement of the On-Road Turbulence Environment Experienced by Heavy Duty Vehicles
Bibliographic record
Abstract
Terrestrial winds play an important role in affecting the aerodynamics of road vehicles. Of increasing importance is the effect of the unsteady turbulence structure of these winds and their influence on the process of optimizing aerodynamic performance to reduce fuel consumption. In an effort to predict better the aerodynamic performance of heavy-duty vehicles and various drag reduction technologies, a study was undertaken to measure the turbulent wind characteristics experienced by heavy-duty vehicles on the road. To measure the winds experienced on the road, a sport utility vehicle (SUV) was outfitted with an array of four fast-response pressure probes that could be arranged in vertical or horizontal rake configurations that provided measurements up to 4.0 m from the ground and spanning a width of 2.4 m. To characterize the influence of the proximity of the vehicle on the pressure signals of the probes, the SUV and its measurements system was calibrated in a large wind tunnel. On-road measurements of the turbulence intensities, turbulence length scales, wind spectra, and spatial correlations were performed. Eight days of testing over a two month period in late 2012 were conducted over roads in Eastern Ontario and Western Quebec, Canada. Dates and test routes were selected to provide a variety of conditions. The time-series of on-road turbulence data were segmented and classified based on differences in the terrain roughness, traffic density, and wind strength experienced during the measurements. Of the three classification categories, traffic density provided the greatest influence on the measured turbulence characteristics by modifying the strength of the high-frequency/small-scale turbulence structures in the wind. Conversely, the strength of the terrestrial winds provided a strong influence on the low-frequency/large-scale turbulence. In the near-ground region of the current study (0.5 to 4.0 m), which represents the vertical extent of typical heavy duty vehicles on the road, the turbulence length scales showed a much greater sensitivity to vertical distance than did the turbulence intensity, resulting from damping of the large-scale/low-frequency wind fluctuations near the ground. Based on the frequency of the various conditions experienced on the road, a target set of wind conditions, (intensities, length scales, spectra, correlation lengths) are recommended that differ from previously-published recommendations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".