Impact of wind profiles on ground-generation airborne wind energy system performance
Bibliographic record
Abstract
Abstract. This study investigates the performance of pumping-mode ground-generation airborne wind energy systems (AWESs) by determining cyclical, feasible, power-optimal flight trajectories based on realistic vertical wind velocity profiles. These 10 min profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the trajectory. To reduce the computational cost, representative wind conditions are determined based on k-means clustering. The results describe the influence of wind speed magnitude and profile shape on the power, tether tension, tether reeling speed, and kite trajectory during a pumping cycle. The effect of mesoscale-simulated wind profiles on power curves is illustrated by comparing them to logarithmic wind profiles. Offshore, the results are in good agreement, while onshore power curves differ due to more frequent non-monotonic wind conditions. Results are references against a simplified quasi-steady-state model and wind turbine model. This study investigates how power curves based on mesoscale-simulated wind profiles are affected by the choice of reference height. Our data show that optimal operating heights are generally below 400 m with most AWESs operating at around 200 m.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".