Numerical assessment of a deformable trailing-edge flap on aerodynamic load control of a pitching S809 airfoil using OpenFOAM
Bibliographic record
Abstract
Due to the unsteady nature of the flow around horizontal-axis wind turbines, the blades are subjected to severe unsteady and fatigue loads. This necessitates an in-depth aerodynamic analysis of flow control techniques to enhance the performance of a wind turbine as well as the lifetime of its components. Using OpenFOAM package in this study, a series of two-dimensional incompressible simulations are performed to present a deeper insight into the aerodynamic characteristics of an oscillating deformable trailing-edge flap, as a promising flow control device, in a sinusoidal pitching motion of an S809 airfoil. Herein, it is of particular interest to investigate the effects of deformable trailing-edge flap size, oscillation frequency, and the phase shift with reference to airfoil motion on lift and drag hysteresis loops. For this purpose, a pure-pitching motion of an S809 airfoil without flap deflection is considered as the benchmark problem in which the airfoil oscillates in the near-stall region at [Formula: see text]. After validation and verification of our simulations through comparison against the corresponding experimental and numerical work, a comprehensive investigation is conducted to study the effects of the aforementioned parameters on the aerodynamic loads. Our results reveal the fact that an out-of-phase deflection of the deformable trailing-edge flap with a frequency equal to the airfoil frequency can significantly mitigate the fatigue load. Under these circumstances, an increase in the deformable trailing-edge flap size can also help the airfoil experience less-severe loads in a cycle of motion. Furthermore, higher values of deformable trailing-edge flap frequency or other values of phase shift except the out-of-phase oscillation cannot alleviate fatigue loads. An airfoil under these conditions can, however, enhance the resultant load required for a blade rotation in the case of low wind periods.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".