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Record W3213407785 · doi:10.18280/jesa.540514

Performance Investigation of Small Wind Turbine Installed over a Pick up Vehicle to Charge an Electric Vehicle Battery

2021· article· en· W3213407785 on OpenAlexvenueno aff
Gashaw Arega Anagie, Abdulkadir Aman Hassen, Yihun T. Sintie

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsTurbineWind powerAirfoilAutomotive engineeringSmall wind turbineWind speedHypersonic wind tunnelMarine engineeringEngineeringWind tunnelEnvironmental scienceAerospace engineeringElectrical engineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

Wind energy is a vital energy free from pollution and freely available energy in our world. The purpose of this research is performance investigation of small horizontal axis wind turbine installed at the top of a pickup vehicle and the power generated from this wind turbine is used to charge the batteries of the vehicle. Permanent magnet generator is selected for the experiment as well as the selected type of the blade is NACA 4412 and its angle of attack is 6°. The angle of attack is determined by using Qblade software at the maximum lift to drag ratio of NACA 4412 airfoil. The blades of wind turbine are fabricated from wood because wood have excellent fatigue properties. The comparison between analytical analysis and experimental test is done based on the reaction wind speed, in order to achieve good matching between analytical analysis and experimental test. Finally, the power output from the wind turbine is used for charging the vehicle battery and the charging process is controlled by regulator. The maximum powers determined from the experimental and theoretical analysis are 334 W and 437 W at reaction wind speed of 28.6m⁄s respectively at the maximum vehicle speed of 25m⁄s.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicElectric Vehicles and InfrastructureFrench-language works237,207