A Novel Technique to Determine Free-Stream Velocity from Ground-Based Anemometric Measurements During Track Tests
Bibliographic record
Abstract
A novel method was developed to predict the free-stream velocity experienced by a traveling vehicle based on track-side anemometric measurements. The end objective of this research was to enhance the reliability of the prediction of free-stream conditions in order to improve the accuracy of aerodynamic drag coefficient (CD) assessments from track tests of surface vehicles. Although the technique was applied to heavy-duty vehicles in the present work, it is equally applicable to any vehicle type. The proposed method is based on Taylor’s hypothesis, a principle applied in fluid mechanics to convert temporal signals into the spatial domain. It considers that the turbulent wind velocity fluctuations measured at one point are due to the "passage of an unchanging pattern of turbulent motion over the point". The method is applied to predict the wind velocity that the vehicle will experience as it encounters a wind pattern detected earlier by an anemometer located upwind. The proposed technique has the potential to enhance the accuracy of free-stream velocity predictions aimed at calibrating a vehicle-mounted anemometer. A sensitivity analysis highlights the errors on the predicted free-stream speed and yaw angle that result from errors in the evaluation of the terrestrial wind speed and direction. Moreover, considering that a surface vehicle travels through the atmospheric boundary layer, it is demonstrated that using track-based anemometers to calibrate a vehicle-mounted anemometer installed at a different height leads to incoherency and can cause substantial errors in the evaluation of free-stream conditions. It is recommended that all ground-based anemometers be installed at the same reference height relative to local ground, as the vehicle-mounted anemometer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".