CFD Water Management Design for a Passenger Coach with Correlation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it