On the Transient Response of Rotors and Autorotating Seeds in Gusty Flows
Bibliographic record
Abstract
Low-inertia rotors are present in quadrotors, micro aerial vehicles, and small wind and tidal turbines, to name but a few examples. The common practice of modelling the unsteady behaviour of low-inertia rotors using quasi-steady assumptions typically means poor rotor performance in unsteady (turbulent) flow environments. This thesis explores the behaviour of low-inertia rotors (and associated flow physics) so as to improve the unsteady performance of generic low-inertia rotor systems. Furthermore, lessons from the robust autorotation of samaras (e.g. maple seeds) in unsteady wind environments are extracted with the potential of applying such lessons in the design of efficient rotor systems. The four studies in this thesis experimentally characterize the unsteady response of low-inertia rotors and autorotating samaras experiencing a sudden change in flow, i.e. an axial gust. Low-inertia rotors are found to produce higher power output during the gust than for quasi-steady operation. To better describe the unsteady response of low-inertia rotors, a new dimensionless group, defining the influence of the rotor moment of inertia relative to the flow inertia, is introduced. Additionally, the kinematics of samaras experiencing canonical gusts, and related aerodynamic mechanisms, are explored experimentally using natural samaras as well as a samara-abstracted rotor. It is shown that natural samaras, and the samara-abstracted rotor, exhibit robust rotation under a variety of gust conditions.
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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 | 0.001 |
| 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".