CFD Water Management Design for a Passenger Coach with Correlation
Bibliographic record
Abstract
Side window clarity and its effect on side mirror visibility plays a major role in driver comfort. Driving in inclement weather conditions such as rain can be stressful, and having optimal visibility under these conditions is ideal. However, extreme conditions can overwhelm exterior water management devices, resulting in rivulets of water flowing over the a-pillar and onto the vehicle’s side glass. Once on the side glass, these rivulets and the pooling of water they feed, can significantly impair the driver’s ability to see the side mirror and to see outwardly when in situations such as changing lanes. Designing exterior water management features of a vehicle is a challenging exercise, as traditionally, physical testing methods first require a full-scale vehicle for evaluations to be possible. Additionally, common water management devices such as grooves and channels often have undesirable aesthetic, drag, and wind noise implications. Being able to detect water management issues such as A-pillar overflow, as well as to develop strategies to resolve them in parallel with early design cycle exterior aerodynamic development, is highly desirable for this reason. This paper details a collaborative effort where a CFD method is first validated against on road testing and then applied to a design study. The Lattice Boltzmann code based results presented show an excellent correlation with on-road test data. One successive design variants is created, with the design process being driven by the understanding provided by the CFD results.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".