Modeling tidal turbine farm with vertical axis tidal current turbines
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
A tidal current turbine is a device for harnessing energy from marine currents, and functions in a manner similar to a wind turbine. A group of tidal current turbines distributed in a site in ocean is called a tidal turbine farm. Among all tidal current energy extraction schemes, turbine farm is regarded as among the most promising turbine configurations; in fact, of turbine farms are extensively employed in the wind power industry. Wind farm planning and modeling approaches cannot be fully transferred to tidal farms, however, because of the complexities involved in modeling the underwater tidal turbine. This study develops a framework for planning a tidal turbine farm system with vertical axis tidal current turbines. This framework is intended to be used by energy planners in the early design stage. An approach for selecting the optimal design among alternative tidal turbine farm designs is proposed whereby the attractiveness of the alternatives is evaluated based on cost effectiveness. Where possible, experience gained from analysis of existing offshore wind farms is applied. The state of the art of tidal turbine design and wind farm planning are reviewed, and a planning framework for selecting the optimal tidal farm design is provided by identifying the important mathematical modeling procedures and elements. Considering the particular design of the vertical axis tidal turbine, a simplified relationship of turbine distribution and turbine farm efficiency is developed. As a case study, numerical simulation results are presented for environmental conditions offshore of British Columbia, Canada.
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