2D Dynamic Stall Simulations with Time-Varying Freestream Representative of Helicopter Flight
Bibliographic record
Abstract
Dynamic stall of helicopter rotor blades occurs predominantly on the retreating rotor blade in high-speed forward flight.It occurs at very fast (or dynamic) change of the angle of attack of an airfoil, leading to lift, drag and pitching moment loads greatly exceeding those at slow (or quasi-steady) changing of angle of attack.The aerodynamic loads generated during dynamic stall are a major source of blade vibration, such that their There were many researchers, namely Kobra Gharali, Peter Gerontakos, Guillaume Martinat, Louis Gagnon who were kind enough to correspond with me about their work, which became the foundation of my own.I would also like to thank the OpenFOAM community, without whom the use of this software would not have been possible.The documentation of this software is very limited, as is the number of people with experience using it, and as a result new users are heavily dependent on the kindness of strangers on the message boards to decipher how to use the software.To the numerous anonymous users who assisted me: I hope that I can in turn pay it forward with the knowledge I have gained.Many thanks to Neil McFadyen for setting up a cluster for my cases to run remotely, and Nancy Powell for her assistance throughout the duration of this degree.I also wish to acknowledge the financial
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".