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Record W2899602251 · doi:10.5539/jas.v10n12p469

Evaluation of a Vertical Axis Wind Turbine for Use in Rural Areas

2018· article· en· W2899602251 on OpenAlexvenueno aff
Rosemar Cristiane Dal Ponte, Enerdan Fernando Dal Ponte, Carlos Eduardo Camargo Nogueira, Jair Antônio Cruz Siqueira, Divair Christ

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerElectricityAlternatorElectricity generationAutomotive engineeringTurbineElectric generatorEnvironmental scienceElectric potential energyElectric powerServomotorMechanical energyElectrical engineeringMechanical engineeringEngineeringPower (physics)

Abstract

fetched live from OpenAlex

With the constant increase in the need for electricity, the use of wind energy emerges as an alternative that is capable of meeting these demands. Considering that several regions in Brazil have a great potential for wind power, it is necessary to develop technologies and investments to ensure the growth of this energy source. The purpose of this project was to study the technical and economic feasibility of a vertical axis wind turbine, which employed a washing machine motor, a servomotor and an alternator for electricity generation, in order to verify which generation system presents better efficiency. Additionally, the unit costs of the energy produced in each generation system were determined and compared to the value of the electricity tariff charged by the concessionaire for rural consumers. Based on the collected data relating to voltages and electric currents, the power and the wind-mechanical, mechanical-electrical and wind-electrical efficiencies of each generator system were calculated, allowing a comparison between these values. The alternator presented the best wind-mechanical efficiency (5.02%) and the best wind-electrical efficiency (0.47%). The washing machine motor showed the best mechanical-electrical efficiency (11.33%). The results showed that the systems have little efficiency in the generation of electricity, and the cost of energy generated indicates values much higher than those practiced by the local electricity concessionaire.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.117

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.281
Teacher spread0.248 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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