Impact of Precipitation Drag on a Road Vehicle
Bibliographic record
Abstract
Road vehicles in the real world experience aerodynamic conditions that might be unappreciated and omitted in wind-tunnel experiments or in numerical simulations. Precipitation can potentially have an impact on the aerodynamics of road vehicles. An experimental study was devised to measure, in a wind tunnel, the impact of rain on the aerodynamic forces of the DrivAer research model. In this study, a rain system was commissioned to simulate natural rain in a wind-tunnel environment for full-scale rain rates between about 8 and 250 mm/hr. A 30%-scale DrivAer model was tested with and without precipitation for two primary configurations: the notch-back and estate-back variants. In addition, mirror-removal and covered-wheel-well configurations were investigated. The results demonstrate a distinct relationship between increasing rain intensities and increased drag of the model, providing evidence that road vehicles experience higher drag when travelling in precipitation conditions. At the lowest rain rates examined, representing moderate rain conditions equivalent to about 8 and 29 mm/hr full scale, drag increases on the order of 2% to 4% were measured. Drag increases in excess of 10% were observed for the highest rain rate tested, which represented an unrealistically-high precipitation condition equivalent to about 250 mm/hr full scale. A non-linear increase in drag with rain rate was observed, suggesting that multiple mechanisms of rain-induced drag were present during the experiments. Lift- and side-force variations did not manifest any trends that were beyond the estimated experimental uncertainty. Therefore precipitation is likely not an influence towards aerodynamic stability. Within the experimental uncertainty, the results did not show any evidence of rain affecting the drag changes associated with modifications to the vehicle shape.
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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 | 0.000 | 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".