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Record W2766982476 · doi:10.1109/oceanse.2017.8084998

Extracting energy from tidal currents: The ocean response at multiple space and time scales

2017· article· en· W2766982476 on OpenAlexaboutno aff
Michela De Dominicis, Rory O’Hara Murray, Judith Wolf

Bibliographic record

VenueOCEANS 2017 - Aberdeen · 2017
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilNatural Environment Research CouncilSight Research UK
KeywordsTidal powerMarine energyEnvironmental scienceSink (geography)Hydrology (agriculture)GeologyRenewable energyMarine engineeringEngineeringGeography

Abstract

fetched live from OpenAlex

Tidal stream energy is a reliable and predictable low-carbon energy source. Over the next few years the first arrays of multiple tidal stream turbines will be deployed mostly in UK, French and Canadian waters. The potential environmental effects should first be examined, in order to scale and site them appropriately. Large theoretical arrays of tidal stream turbines were designed for Scottish Waters (UK), to quantify the available power and the ocean response to its extraction. A comprehensive assessment of the tidal energy resource realistically available for electricity generation and the study of the potential environmental impacts associated with its extraction in Scottish Waters, can then lead the way to further development in different countries with potential tidal stream energy sources. In order to examine both local (100 km) spatial scales, the Scottish Shelf Model, an unstructured grid three-dimensional FVCOM (Finite Volume Community Ocean Model) implementation, is a useful tool, since it covers the entire NW European Shelf, but with a high resolution where the tidal stream energy is extracted. The arrays of tidal stream turbines were implemented in the model using the momentum sink approach, in which a momentum sink term represents the loss of momentum due to tidal energy extraction. A typical annual cycle of the NW European Shelf hydrodynamics was reproduced by the SSM model and compared with the same period perturbed by tidal stream energy extraction. The power extracted by the tidal stream arrays was estimated, taking into account the tidal stream energy extraction feedbacks on the flow and considering the realistic operation of a generic tidal stream turbine, which is limited to operate in a range of flow velocities due to technological constraints. The ocean response to tidal stream power extraction was then analysed at the temporal scale of a spring-neap tidal cycle and, for the first time, on longer term seasonal timescales. It is shown that the very large tidal stream arrays can introduce detectable changes to the tidal elevation, marine currents and ocean stratification patterns. Tidal elevation mainly increases upstream of the tidal farms locations (considering the direction of propagation of the tidal wave), while a decrease in the mean spring tidal range is observed downstream, along the UK East Coast and also in the Irish Sea. Marine currents, both tidal and residual flows, are also affected. They can slow down due to the turbines' action or speed up due to flow diversion and blockage processes, on both a local and regional scale. The strongest signal in tidal velocities is an overall reduction, which can in turn decrease the energy of tidal mixing and perturb the seasonal stratification on the NW European Shelf. Although the strength of summer stratification has been found to slightly increase, the extent of the stratified region does not greatly change, thus suggesting the enhanced biological and pelagic biodiversity hotspots, e.g. tidal mixing front locations, are not displaced. Such large-scale tidal stream energy extraction is unlikely to occur in the near future, since very large numbers of devices are required, but such potential changes should be considered when planning future tidal energy exploitation. It is likely that large scale developments around the NW European shelf will interact and could, for example, intensify or weaken the changes predicted here, or even be used as mitigation measures (e.g. coastal defence) for other changes, e.g. effects of climate change.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.237
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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