Examination of the Flow Separation Characteristics Around a Streamlined Axisymmetric Shape
Bibliographic record
Abstract
Using computational fluid dynamics (CFD), a study was conducted to predict the hydrodynamic forces and moments on an axisymmetric body over a range of yaw angles and Reynolds numbers. Computational results for hydrodynamic forces and moments show good agreement with experimental data, being within the experimental uncertainty range at most yaw angles. Deviations outside of the uncertainty range occurred for the lateral (Y) force values at yaw angles greater than 15 degrees. The development of the after-body vortex shows good agreement with experimental observation. Primary and secondary separation points and shear stress streamline behaviour are also compared with experiment data at a yaw angle of 24 degrees. Results are discussed with a view to identifying flow features critical to the development of new force estimation methods. The after-body vortex, at increasing yaw angles, influences the overall force and moment predictions through a complex interaction between the transport of after-body vorticity and the detachment/reattachment locations of the boundary layer. Adequate modeling of this after-body region is increasingly important at high yaw angles. One of the most important features that influences the overall forces and moments is the circumferential position of shear layer detachment and reattachment, which have a direct impact on the pressure distribution along the body.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".