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Record W7043339971

IN-SITU TESTING OF A DARRIEUS HYDRO KINETIC TURBINE IN COLD CLIMATES

2016· article· en· W7043339971 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTurbineCold climateKinetic energyElectricity generationExtreme ColdHydraulic turbinesPower (physics)Hydro powerFlow (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

There is a significant potential for kinetic turbine technology in Canada. An estimated 225 GW of power have been identified in wave and tidal energy with river energy yet to be adequately assessed. Manitoba is an ideal location for river turbines, and thus this study was conducted to demonstrate the turbine’s feasibility in cold climates. Frazil ice is a cause for concern in northern regions because it reduces the output power of larger hydro installations and can adversely impact kinetic turbine installations. Along with environmental concerns, a 5 kWe Darrieus turbine was evaluated for its performance. Sources of power loss were quantified in this study. It was found that the turbine’s support arms contributed a significant loss of up to 66%. The non-ducted Darrieus design self-started in a flow of 2 m/s and saw a peak power coefficient of 0.35 while producing reliable and consistent power to the grid throughout the winter and summer months. ii

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 score0.492
Threshold uncertainty score0.950

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 head0.015
GPT teacher head0.187
Teacher spread0.173 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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