Experimental study of the wake produced by single and multiple cross-flow turbines
Bibliographic record
Abstract
Hydrokinetic turbines, which convert the kinetic energy of flowing water into electrical energy, are increasingly being deployed in arrays, much like multiple wind turbines are commonly organized into a wind farm. Deploying multiple devices in an array can increase the amount of energy produced from a site and tends to make project economics more favourable. Despite growing interest in hydrokinetic turbine arrays, relatively few researchers have studied arrays and limited information exists concerning the character of the turbulent flows within arrays and methods for optimizing array layout, particularly for cross-flow turbines. This paper presents results from a set of experiments conducted in a current flume in which the wakes produced by a single cross-flow turbine and pairs of identical cross-flow turbines were measured in detail. Two different ambient turbulence intensities were studied so that the effect of turbulence level on wake recovery could be quantified. The velocity data has been analyzed to identify the mean velocity and the turbulence intensity in the turbine wake(s). These experiments form one component of a larger study that aims to develop numerical methods and guidelines to help project developers optimize the design of turbine arrays.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".