Considerations for the Wind Tunnel Simulation of Tractor-Trailer Combinations: Correlation of Full- and Half-Scale Measurements
Bibliographic record
Abstract
The 9-meter wind tunnel of the National Research Council (NRC) of Canada is commonly employed in testing of class 8 tractors at full- and model-scales. In support of this work a series of tests of an identical model at full- and half-scale were performed to investigate some of the effects resulting from simulation compromises. Minimum Reynolds Number considerations drive the crucial decisions of what scale and speed to employ for testing. The full- and half-scale campaigns included Reynolds Number sweeps allowing conclusions to be reached on the minimum Reynolds number required for testing of fully-detailed commercial truck models. Furthermore the Reynolds sweeps were repeated at a variety of yaw angles to examine whether the minimum Reynolds Number was a function of yaw angle and the resulting flow regime changes. The test section of the NRC 9-meter wind tunnel is not sufficiently long to accommodate a full-scale tractor and a typical trailer length of 48′ or more. Most of full-scale testing is therefore conducted with a more manageable 28′ trailer. To quantify the effects of this simplification the half-scale model was tested with representations of 28′ and 48′ trailers. The changes in the baseline drag curves will be presented along with the effect of trailer devices, such as skirts, on the different lengths of trailer.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".