MétaCan
Menu
Back to cohort
Record W1977629521 · doi:10.1088/1755-1315/22/6/062002

Development of a hydro kinetic river turbine with simulation and operational measurement results in comparison

2014· article· en· W1977629521 on OpenAlexaffabout
Andreas Ruopp, Albert Ruprecht, Stefan Riedelbauch, G Arnaud, I Hamad

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2014
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsInflowDraft tubeTurbineMarine engineeringRotational speedPressure dropKinetic energySection (typography)Acoustic Doppler current profilerEngineeringMechanical engineeringComputer scienceMeteorologyMechanicsCurrent (fluid)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

The development of a hydro-kinetic prototype was shown including the compound structure, guide vanes, runner blades and a draft tube section with a steeply sloping, short spoiler. The design process of the hydrodynamic layout was split into three major steps. First the compound and the draft tube section was designed and the best operating point was identified using porous media as replacement for the guide vane and runner section (step one). The best operating point and the volume flux as well as the pressure drop was identified and used for the design of the guide vane section and the runner section. Both were designed and simulated independently (step two). In step three, all parts were merged in stationary simulation runs detecting peak power and operational bandwidth. In addition, the full scale demonstrator was installed in August 2010 and measured in the St. Lawrence River in Quebec supporting the average inflow velocity using ADCP (Acoustic Doppler Current Profiler) and the generator power output over the variable rotational speed. Simulation data and measurements are in good agreement. Thus, the presented approach is a suitable way in designing a hydro kinetic turbine.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.299

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 head0.023
GPT teacher head0.212
Teacher spread0.189 · 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 designObservational
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

Citations10
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueIOP Conference Series Earth and Environmental ScienceSame topicWind Energy Research and DevelopmentFrench-language works237,207