Preliminary investigation of the impact of precipitation on the aerodynamics of road vehicles
Bibliographic record
Abstract
Road vehicles in the real world experience aerodynamic conditions that are often omitted in wind-tunnel or numerical simulations. Precipitation can potentially have an impact on the aerodynamics of road vehicles. This report is a first step towards gathering the knowledge required for developing methodologies to assess the aerodynamics of road vehicles under precipitation conditions. A majority of the available literature has been dedicated to aviation applications, with little work done on how precipitation influences aerodynamic performance. A review of experimental and numerical approaches to assess precipitation impacts to the aerodynamic performance of aircraft has provided an essential foundation for assessment of road vehicles since the mechanisms of interaction are presumed to be similar. For aircraft wings, rain can impact the aerodynamics by three different mechanisms: the exchange of momentum between rain droplets impacting the surface of the wing; momentum loss and subsequent deceleration of the boundary-layer flow due to acceleration of rain particles and splash-back of particles; and added surface roughness due to uneven distribution of a thin water film forming on the surface. It is expected that these three mechanisms are all present in the case of a road vehicle, but their relative magnitudes are likely different. Recommendations are provided for assessing rain-induced drag of road vehicles using both experimental and numerical techniques.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".