Bibliographic record
Abstract
Our goal was to find expected pressure around different wingtip shapes to predict vortice behavior. This project focused on aerodynamics, specifically the location where high and low-pressure air mixes over a lifting surface. High-pressure air mixes with the low-pressure air at the wingtip of a plane creating vortices that cause drag, which wastes fuel and slows down the aircraft. Not only is this bad for the environment, but it increases the cost of flight and affects the distance that larger planes can fly ahead of smaller planes due to wake turbulence. As planes have gotten lighter, faster, and safer, the issue of wingtip vortices and drag has continued to be a problem. The approach we used to answer this problem was to select an applicable data set using continuous machine learning models and later, discrete models to predict a pressure coefficient above the wing. We combined multiple datasets from the same research paper created by NASA to have numerous factors for the machine learning model to predict. As a result, we produced accurate static pressure predictions with 80% to 90% accuracy. Even more accurate were our model recall scores which were within 99%. As a result of the work done on this project, accurate predictions of expected pressure over an airfoil are achievable. With only a few input variables about speed and dimensions, an accurate static pressure can be found.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".