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Record W4296078695 · doi:10.1063/5.0104471

Numerical model of a tidal current acceleration structure

2022· article· en· W4296078695 on OpenAlexaff
Binayak Lohani, Derek Foran, Abdolmajid Mohammadian, Ioan Nistor

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.

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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
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

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

Citations2
Published2022
Admission routes1
Has abstractyes

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