Large-Scale Vehicle-Wake Characterization Using a Novel, Single-Camera Particle Tracking Technique
Bibliographic record
Abstract
The aerodynamic forces experienced by vehicles depend on a variety of factors including wind direction, traffic, and roadside vegetation. Such complex boundary conditions often result in unsteady flow separation and the formation of large-scale coherent structures, which, in turn, significantly influence the aerodynamics of following vehicles. To gain a deeper understanding of the unsteady behaviour of such vehicle wakes under large-scale conditions, a time-resolved field measurement technique is required. Existing methods, such as tomographic particle image velocimetry and three-dimensional particle tracking velocimetry are unfortunately quite limited at these scales. Furthermore, such techniques require complex multi-camera calibrations, hazardous lasers, and optical access from many vantage points. To date, the high costs, long set-up times, and prohibitive safety measures for lasers limit the application of classical field-measurement techniques in industrial automotive wind tunnels. To overcome the aforementioned issues, a simple and efficient single-camera approach to perform large-scale time-resolved three-dimensional flow-field measurements is proposed. The flow is seeded with centimeter-sized soap bubbles, which are illuminated via pulsed LED arrays. The feasibility of the novel measurement approach was tested in an industrial wind tunnel (cross-section 9.1 m × 9.1 m) at the National Research Council Canada. The test successfully captured the vortical structure in the wake of a 30%-scale tractor-trailer model at a 9° yaw angle with a measurement volume of approximately 4.0 m × 1.5 m × 1.5 m. Long tracks of up to 90 time steps were captured, along which the twisting motions help identify the vortex wake near the trailer. These sparse tracks not only allow for time-resolved analysis of the wake but also provide insights into Lagrangian transport. Time-averaged results are derived from the Lagrangian data and showed good agreement with a comparative experiment measuring the wake flow behind a 9° yawed 1/15-scale tractor-trailer model using pressure probes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".