A Numerical Exploration of Parameter Dependence in Power Optimal Flapping Flight
Bibliographic record
Abstract
A computational framework for analyzing and designing efficient flapping flight vehicles is presented. Two computational tools are considered: a Betz Criterion code proposed by Hall et. al., and an accelerated, unsteady, potential flow solver. The parameters considered in this paper are: the flapping frequency, the flapping amplitude in both up-down and forward-aft directions, and the addition of a mid-wing hinge for articulated flapping flight. The flapping kinematics are represented using harmonics. Three numerical experiments are examined for the flapping flight analysis. The first experiment involves sweeping through a basic flapping flight parametric design space. The second experiment minimizes flight power at a given flight condition using a quasi-Newton optimization. The third experiment demonstrates the conversion of the problem from a wake only analysis to a 3-D flapping wing geometry. Φ up-down flapping angle Ψ fore-aft (sweep) angle φ phase lag in Φ ψ phase lag in Ψ b span c chord s arc length along wing sj arc length position of joint t time XY Z Cartesian location in space uvw Cartesian velocity components U wing center-point velocity (in −X direction) V total local velocity ω primary flapping frequency ¯sj fractional joint position ( = 2sj/b) µ advance ratio ( = U/(ωb) Γ wing circulation cℓ section lift coefficient ( = 2Γ/cV) cd section drag coefficient constant coefficient of profile-drag polar cd0
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".