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Record W4296078695 · doi:10.1063/5.0104471

Numerical model of a tidal current acceleration structure

2022· article· en· W4296078695 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Renewable and Sustainable Energy · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFroude numberTidal powerInflowTurbineCurrent (fluid)MechanicsAccelerationRenewable energyHydraulic structureEngineeringEnvironmental scienceFlow (mathematics)Marine engineeringMechanical engineeringGeotechnical engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Advancements in technology have led to a rapid rise in the use of renewable energy sources in the past 25 years. The current work focuses on the potential of a novel hydraulic technology to contribute toward sustainable energy production. The tidal current acceleration structure is a simple structure that uses the basic principle of the Venturi effect in low-speed tides and rivers to accelerate the flow and, in turn, extract energy using turbines. The primary aim of the present study is to understand to what extent this newly proposed tidal flow structure is suitable for real applications. The shear stress transport k–ω model was utilized, and the parametric analysis based on angle variation, Froude number, and bed roughness was undertaken to optimize the performance of the structure. The potential power that could be extracted by an in-stream turbine was then estimated using actuator disk theory. The performance of the structure significantly increased for the configuration having the ratio of the opening and contraction width of 2.66.

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.

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 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: none
Teacher disagreement score0.663
Threshold uncertainty score0.291

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)0.0000.000

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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