MétaCan
Menu
Back to cohort
Record W2020037291 · doi:10.1109/oceans.2014.7003267

Cross-flow helical turbine for energy production in reversing tidal and ocean currents

2014· article· en· W2020037291 on OpenAlexaff
Greg Walsh, R. Keough, V. Mullaley, Harold Sinclair, M.J. Hinchey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsMemorial University of Newfoundland
FundersU.S. Department of Energy
KeywordsTurbineTorquePower (physics)Rotational speedAngular velocityReversingMechanicsControl theory (sociology)Flow (mathematics)BrakeTidal powerWater turbineElectricity generationPID controllerSimulationController (irrigation)Marine engineeringEngineeringMechanical engineeringComputer sciencePhysicsTemperature controlClassical mechanics

Abstract

fetched live from OpenAlex

This paper presents an investigation into the performance of a helical water turbine in both computer simulated and physical tests. Power curves were developed for both physical and virtual models, as well as angular velocity and power responses over time. The virtual model was tested with a PID controller to simulate real-world speed control in power generation by varying turbine loading. The virtual model responded well to virtual PID control, suggesting a physical model could be controlled by varying the brake torque, or applied load. Both models displayed some small variance in rotational speeds and torque over time, but values tended to oscillate between two asymptotic values. Though neither the virtual nor physical models approached previously demonstrated efficiencies of helical water turbines, both models agreed that peak power generation occurs at approximately 60% of freewheel angular velocity for the stream speeds tested. It is suggested that higher efficiencies may be obtained by changing turbine configuration, and in the case of virtual models, using an even finer resolution mesh for computational fluid dynamics analysis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.257

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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 teacher head, 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicWind Energy Research and DevelopmentFrench-language works237,207