On the Determination of Propulsive Characteristics of a Flapping Airfoil with Advanced ALE Method
Bibliographic record
Abstract
Flapping wings for flying and oscillating fins for swimming stand out as the most complex yet efficient propulsion methods found in nature. Understanding the phenomena involved is a great challenge generating significant interests, especially in the growing field of Micro Air Vehicles. Even if an increasing body of litterature is now available, much research needs to be done to properly simulate the propulsive phenomenon of flapping airfoils. The flexibility of biological foils must be replicated and the airfoil motion induced by the generated thrust must be accounted for. This paper presents an effective computational framework for simu- lating the propulsive characteristics of a forward-moving flexible flapping airfoil. We use a direct monolithic ALE formulation for the unsteady interaction of a viscous incompressible 2D flow with an elastic structure undergoing large displacements (geometric non-linearities). A point mass approach allows to compute the motion of the airfoil due to the aerodynamic forces induced by airfoil oscillations. The problem is solved in an implicit manner using a Newton-Raphson pseudo-solid finite element approach. High-order implicit Runge-Kutta time integrators are implemented to improve the accuracy and reduce the computational cost. After some verifications of the computational framework with a flapping rigid NACA0015 airfoil, we study the effects of the motion parameters and the flexibility on the propulsion efficiency.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".