Design of Optimal Airfoils for Crosswind Kite Power Systems
Bibliographic record
Abstract
This paper introduces a novel framework for designing optimal airfoils for crosswind kites which are tethered flying systems used to harness high-altitude wind energy. The improved geometric parameter method, a state-of-the-art airfoil design approach, is employed here for the first time in the context of airborne wind energy airfoil design. The nondominated sorting genetic algorithm is used as the optimization method, and XFOIL is adopted to obtain aerodynamic lift and drag coefficients of the airfoils. Pareto-optimal fronts and the corresponding optimal airfoil profiles at various maximum thickness ratios are obtained for a baseline system that neglects three-dimensional flow effects and tether drag. For the first time in the literature, the effects of induced drag due to finite aspect ratio kites on the optimal airfoils are examined. Additionally, the effects of including the tether drag on the optimal solutions are explored. It is found that when the induced drag is included optimal airfoils feature a cusped trailing edge. On the other hand, when the tether drag is considered, the optimal airfoils are found in shape to be reminiscent of flapped airfoils, suggesting a multi-element airfoil design. Finally, unlike most studies in the literature, the present work conducts post-optimization Reynolds-averaged Navier–Stokes flow simulations to gain deeper insights into the aerodynamic performance of the optimized airfoils and to provide comparisons with XFOIL results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".