Optimized configuration with economic evaluation for shrouded vertical axis wind turbines applicable for urban structures
Bibliographic record
Abstract
Abstract In recent urban buildings, there is an interest in using wind turbines inside the buildings. Thus, the rotors of wind turbines are shrouded by the walls of the buildings. A design for using wind turbines in urban areas is to use shrouded turbines on the roof of a building. Since vertical axis wind turbines (VAWTs) with low environmental impacts are recommended for energy supply in urban areas, here, the interaction of two shrouded VAWTs has been studied numerically. Based on the effects of different shroud parts, the diffuser‐shrouded turbines are selected. The arrangement of diffuser‐shrouded VAWTs has been optimized using the response surface method optimization with the Kriging model. The optimization samples are chosen by the design of experiment method to reduce the number of simulations and increase optimization accuracy. The total power coefficient of the optimized cluster is 46.2% higher than the sum of the same individual diffuser‐shrouded turbines and 149.6% higher than the sum of individual bare VAWTs. The economic assessment shows that the levelized cost of energy of the optimized cluster of two diffuser‐shrouded VAWTs is reduced by about 40% compared to two bare VAWTs.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".