Evaluation of an Unsteady Yaw, Gust, and Broadband Turbulence Generation System for Closed-Loop Automotive Wind Tunnels
Bibliographic record
Abstract
This study investigates the flow characteristics in the test section of a model-scale, three-quarters open-jet, closed-loop return wind tunnel equipped with a novel device featuring three subsystems to generate transient yaw, gusts, and turbulence. The effect of each subsystem on the resulting turbulent and unsteady flows is evaluated individually and simultaneously. It is demonstrated that this new turbulence generation system can generate yaw distributions with standard deviations ranging from 2.1° to 8.0°. This replicates a wide range of on-road yaw behavior. Additionally, the subsystems can activate transient yaw events and unsteady gusts. Frequency sweeping was demonstrated to fill a wide range of low-frequency spectra, which helps recreate the on-road flow spectra in wind tunnels. Unsteady gusts of more than 15% of the mean flow velocity were achieved. The active turbulence subsystem generates turbulence levels from a few percent, passively, to over 20% intensity levels actively, with tailorable levels depending on input parameters to the active grid. Combined, the subsystems were demonstrated to achieve a wide range of yaw distributions with different standard deviations and features of the on-road turbulence spectrum, from low-frequency events to broadband turbulence with significant inertial subrange within the model-scale wind tunnel.
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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 | 0.001 | 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".