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Record W1694361518

Influence of turbulent flow field on power generation

2013· article· en· W1694361518 on OpenAlex
Armin Hamta, Amir Hossein Birjandi, Eric Bibeau

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2013 OCEANS - San Diego · 2013
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTurbulenceWakeMechanicsTurbineCylinderTurbulence kinetic energyOpen-channel flowFlow (mathematics)GeologyMeteorologyPhysicsMarine engineeringMathematicsEngineeringGeometryMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

A 3D mapping technique is introduced to assist in finding optimal site locations for hydrokinetic turbines. The low and high turbulent statistics are mapped out in a open channel flow in which a cylinder is utilized as a source of disturbance. Measurements includes normal and Reynold's shear stresses, turbulent kinetic energy, and average velocity. Power coefficients for a vertical axis hydrokinetic turbine influenced under different wake regions of the cylinder are correspondingly mapped out and illustrated in a 3D matrix. Along with turbulent statistics and turbine performance, verification of the results and data collected are examined via the Frozen Taylor Hypothesis and the use of Taylor Macro-Scales.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.430
Threshold uncertainty score1.000

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)0.0010.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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