Simulation of Atmospheric Turbulence for Wind-Tunnel Tests on Full-Scale Light-Duty Vehicles
Bibliographic record
Abstract
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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".