A Numerical Exploration of Parameter Dependence in Power Optimal Flapping Flight
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it