Ground-generation airborne wind energy design space exploration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. While some Airborne Wind Energy System (AWES) companies aim at small-scale, temporary or remote off-grid markets, others aim to integrate utility-scale, multi-megawatt AWES into the electricity grid. This study investigates the scaling effects of single-wing, ground-generation AWESs from small to large-scale systems, subject to realistic 10-minute, onshore and offshore wind conditions derived from the numerical mesoscale weather research and forecasting (WRF) model. To reduce computational cost, wind velocity profiles are grouped into k = 10 clusters using k-means clustering. Three representative profiles from each cluster are implemented into a nonlinear AWES optimal control model, to determine power-optimal trajectories, system dynamics, as well as instantaneous and cycle-average power. We compare the performance of three different aircraft masses and two sets of nonlinear aerodynamic coefficients for each aircraft size, with wing areas ranging from 10 m2 to 150 m2. We predict size and weight-dependent, optimal AWES power curves, annual energy production (AEP) and capacity factor (cf). Tether impacts, such as power losses associated with tether drag and the tether contribution to total system mass are quantified. Furthermore, we estimate a minimum average cycle-average lift to weight ratio, above which ground-generation AWES can operate, to explore the viable AWES mass budget.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it