Circulation Redistribution in Leading-Edge Vortices with Spanwise Flow
Bibliographic record
Abstract
A low-order model for the circulation budget within a leading-edge vortex (LEV) is proposed, based on one-dimensional species advection. The model is composed of two parts: first, a shear-layer model predicts the circulation feeding rate into the LEV; and second, a spanwise transport model initializes vorticity-containing mass with a finite circulation, allowing circulation to advect along the span with spanwise flow. No empirical data are necessary to inform the results of the model. As a proof of concept, both components of the proposed model are evaluated against a flat-plate delta wing. Using particle image velocimetry, the proposed shear-layer model is found to predict circulation flux into the LEV. Particle-tracking velocimetry is used to validate the spanwise transport of circulation. By allowing a vorticity-containing mass to advect with the spanwise flow, the model automatically satisfies the vorticity transport equation when vortex tilting and viscous diffusion are neglected. Neglecting the vortex tilting and viscous diffusion terms results in an error of approximately 10% of the spanwise advection, such that these terms are within the acceptable tolerance of a low-cost model. Thus, the proposed model is a computationally inexpensive tool for predicting circulation redistribution in flows with specific three-dimensional effects, providing a framework for broader parameter studies going forward.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".