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Record W2317396042 · doi:10.1115/imece2010-39487

Reynolds Number Effects on the Freewheeling Behavior of a High Solidity Vertical River Kinetic Turbine

2010· article· en· W2317396042 on OpenAlexaffabout
Amir Hossein Birjandi, Eric Bibeau

Bibliographic record

VenueVolume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and B · 2010
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSolidityReynolds numberStall (fluid mechanics)MechanicsTip-speed ratioTurbineMathematicsPhysicsGeometryReynolds equationTurbine bladeKinetic energyClassical mechanicsThermodynamicsAerodynamicsComputer scienceTurbulence

Abstract

fetched live from OpenAlex

A four-bladed, squirrel-cage, and scaled vertical kinetic turbine was designed, instrumented and tested in the water tunnel facilities at the University of Manitoba. With a solidity of 1.3 and NACA0021 blade profile, the turbine is classified as a high solidity model. Results were obtained for conditions during freewheeling at various Reynolds numbers. In this study, the freewheeling tip speed ratio, which relates the ratio of maximum blade speed to the free stream velocity at no load, was divided into three regions based on the Reynolds number. At low Reynolds numbers, the tip speed ratio was lower than unity and blades were in a stall condition. At the end of the first region, there was a sharp increase of the tip speed ratio so the second region has a tip speed ratio significantly higher than unity. In this region, the tip speed ratio increases almost linearly with Reynolds number. At high Reynolds numbers, the tip speed ratio is almost independent of Reynolds number in the third region. It should be noted that the transition between these three regions is a function of the blade profile and solidity. However, the three-region behavior is applicable to turbines with different profiles and solidities.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.208
Teacher spread0.202 · 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.

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

Citations3
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueVolume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and BSame topicCavitation Phenomena in PumpsFrench-language works237,207