Simulation of Atmospheric Turbulence for Wind-Tunnel Tests on Full-Scale Light-Duty Vehicles
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">During the past year, a novel turbulence generation system has been commissioned in the National Research Council (NRC) 9 m Wind Tunnel. This system, called the Road Turbulence System was developed to simulate with high fidelity the turbulence experienced by a heavy duty vehicle on the road at a geometrical scale of 30%. The turbulence characteristics that it can simulate were defined based on an extensive field measurement campaign on Canadian roads for various conditions (heavy and light traffic, topography, exposure) at heights above ground relevant not only for heavy duty vehicles but also for light duty vehicles.</div><div class="htmlview paragraph">In an effort to improve continually the simulation of the road conditions for aerodynamic evaluations of ground vehicles, a study was carried out at NRC to define the applicability of the Road Turbulence System to aerodynamic testing of full-scale light duty vehicles. Using the on-road measurements as a guide, it was concluded that the RTS appears to provide a more representative simulation of on-road conditions for wind-tunnel testing, compared with other passive methods such as with rope nets, spires, large turbulence grids or also with active turbulent generation techniques. Although some differences from the target turbulence conditions were found, all spectra measured behind the RTS were found to be within the range of turbulence energy measured during the on-road campaign. As such, the RTS was subsequently used for measurements of two full-scale light-duty vehicles (a large car and a standard sport-utility vehicle) during a test campaign examining the influence of active drag-reduction technologies. Comparison of these data to the equivalent smooth-flow measurements showed different influences of turbulence on the drag behaviour for each of the two vehicles.</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