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Record W4317936705 · doi:10.3986/9789610507109

Hydrodynamic modelling of tidal range energy

2023· book· en· W4317936705 on OpenAlexaboutno aff
Nejc Čož

Bibliographic record

VenueProstor, kraj, čas · 2023
Typebook
Languageen
FieldEarth and Planetary Sciences
TopicAquatic and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTidal powerHydropowerTidal rangeRenewable energyEstuaryRange (aeronautics)Marine energyEnvironmental scienceMeteorologyEngineeringMarine engineeringOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

This book presents a method for hydrodynamic modelling of tidal power plants. In the face of climate change, tidal power plants will play an important role in the transition to renewable energy and in the decarbonisation of society. Tidal energy is not dependent on weather conditions and is fully predictable, allowing accurate forecasts for several years into the future. Its main drawback is that it is limited to areas with high tidal ranges, such as the Atlantic coasts of the UK, France and Canada, as well as other seacoasts around the world. The role of hydrodynamic models in the design of hydropower plants is primarily to understand the impact on the environment and to optimise the operation of the plant itself. The book is divided thematically into three sections. The first section provides a comprehensive overview of the background to tidal power and reviews the available literature. The second section contains a detailed description of the theoretical background and the numerical modelling methodology using the open source software Delft3D. The last section consists of two case studies on which the methodology was tested. They deal with the hydrodynamic impacts of two planned tidal power plants in the UK, namely a barrage in the Severn Estuary and an artificial lagoon in Swansea Bay.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.173
Teacher spread0.147 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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