Currents, Waves and Turbulence Measurement: A View from Multiple Industrial-Academic Projects in Tidal Stream Energy
Bibliographic record
Abstract
Tidal Stream Energy is considered a regular, predictable and dense energy source with potential to make a significant contribution to our future energy needs. Development of the industry, from resource assessment to device design and operation, requires characterisation of the flow environment at a variety of spatial and temporal scales at tidal energy sites. Demand for flow characterisation arises from companies developing, installing and operating tidal turbine prototypes or small arrays in locations from Scotland to France to Canada. Flow characterisation for tidal stream applications relies on the measurement of water velocity at the relevant scales, yet given the non-uniformity of the flow field, no single instrument measures all the necessary data inputs required by the sector. This paper provides an overview of a variety of current, surface wave and turbulence metrics of industrial relevance to tidal stream and discusses methods employed to secure these datasets. The use of variants of acoustic current profilers is presented, which have been utilised and developed on previous and ongoing industrial-academic projects, including ReDAPT (ETI, UK), FloWTurb (EPSRC, UK) and RealTide (EC H2020, EU). These variants feature differing numbers of acoustic transducers and varying geometrical configurations with installations at both seabed locations and atop operating tidal stream energy converters. Ongoing development of advanced sensor configuration is discussed, aiming to achieve resilient, high resolution three-dimensional measurement of mean and turbulent flow tailored for tidal energy applications. The paper gives practitioners and researchers an overview of tidal stream flow characterisation and practical lessons learnt.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".