Modeling of lifting‐device aerodynamics using the actuator surface concept
Bibliographic record
Abstract
Abstract The actuator surface (AS) concept and its implementation within a differential, Navier–Stokes control volume finite‐element method (CVFEM) are presented in this article. Inspired by vortex and actuator disk methods, the AS concept consists of using porous surfaces carrying velocity and pressure discontinuities to model the action of lifting surfaces on the flow. The underlying principles and mathematics associated with AS are first reviewed, as well as their implementation in a CVFEM. Results are presented for idealized 2D cases with analytical solutions, as well as for the 3D cases of a finite wing and an experimental wind turbine. In the case of the finite wing, wake induction is well handled by the model with accurate predictions of induced angles and drag when compared with the Prandtl lifting line model. Comparisons with volume force approaches, often used to model the action of propellers or wind turbine blades in a simplified analysis, show that the AS concept has some interesting advantages in terms of accuracy and respect of flow physics. This new approach is easy and rapid to embed in most computational fluid dynamics (CFD) methods. It is applicable to a wide range of problems involving thin lifting devices like finite wings, propellers, helicopter or wind turbine blades. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".