Bibliographic record
Abstract
A tidal current turbine is a device for harnessing energy from tidal current. A group of tidal current turbines, distributed schematically at a site, is called a tidal turbine farm. A tidal turbine farm has to be located in a confined channel or a straight where consistent high-velocity tidal current flow is available for the cost-effectiveness concern. This narrow geographical condition poses challenges for turbine farm planners to distribute turbines strategically. Turbines’ distribution in a farm affects power generation efficiency and the resultant tidal unit power cost. In this paper, we propose a procedure for predicting energy generation from a tidal turbine farm by investigating the optimal distribution of turbines at a given site. The objective of optimizing the turbines distribution is to maximize the power output efficiency. To fulfill this, we conducted a systematic analysis on power generation from a tidal turbine farm to identify the key factors affecting the optimal tidal turbines distribution with an emphasis on the turbines’ hydrodynamics analysis and briefed the turbine working principle. As a companion paper to Li and Calisal (2007) which discusses the principle of a stand alone turbine, turbine configuration and interactions (i.e. angle of attack, turbine relative distance and turbine size) are extended here. The main assumption of this discussion is that vortex shedding impact is the dominant factor causing the turbine efficiency loss. Considering the turbine design principle, a simplified relationship between turbines distribution and turbine farm efficiency is formulated. Then, numerical simulation results are presented for a given site in British Columbia together with extended general solution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".