Validation of Potential Flow Aerodynamics for Horizontal-Axis Wind Turbines in Steady Conditions using the MEXICO Project Experimental Data
Bibliographic record
Abstract
Potential flow methods are a promising alternative to mainstream wind turbine aerodynamics tools such as blade element momentum methods and grid-based computational fluid dynamics approaches. Potential flow methods are relatively easy to setup and robust with respect to geometry. The advent of the fast multipole method and viscous core modelling brings computational speed and robustness. A C++ library employing a Weissinger lifting line model and tailorable potential flow wake models has been developed under the name LibAero. The wake models employ vortex particles, vortex filaments, and vortex quadrilateral elements. Aerodynamic wake models were validated against experimental data from the MEXICO wind tunnel experiments in steady axial wind conditions. Blade forces and flow field data were compared. The experimental blade forces were post-processed from airfoil pressure tap data, whereas flow field data was post-processed from particle image velocimetry data. The results indicate that LibAero is effective at predicting blade forces, power, and thrust. LibAero is similarly effective to blade element momentum methods for modelling the aerodynamics of standard Danish wind rotors, while having the capability to model non-standard wind rotors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".