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Record W2120581411 · doi:10.1109/ccece.2006.277821

A Technology Review and Simulation Based Performance Analysis of River Current Turbine Systems

2006· review· en· W2120581411 on OpenAlexafffund
Munna Khan, M. Tariq Iqbal, John E. Quaicoe

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandAtlantic Canada Opportunities Agency
KeywordsComputer scienceMATLABTurbineCurrent (fluid)Rotor (electric)ElectricityConvertersDomain (mathematical analysis)Industrial engineeringHydraulic turbinesSystems engineeringControl engineeringMarine engineeringMechanical engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

River current turbines are electromechanical energy converters that harness kinetic energy of a stream of flowing river water to generate electricity. Research in this domain is limited and various concepts are emerging only recently. In this paper, an extensive technology survey and comparison of various system options are discussed in order to formulate a basis for further analysis. Simplified mathematical modeling of an augmentation device and Darrieus type rotor has been carried out. Simulations are done in Matlab and results are given with graphical interpretations. In conclusion, directions for further investigation are given and the potential of this technology is re-stressed

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.025
GPT teacher head0.279
Teacher spread0.253 · 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
GenreReview

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

Citations53
Published2006
Admission routes2
Has abstractyes

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