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Record W4302758075 · doi:10.1139/tcsme-2022-0025

An innovative design of INVELOX wind turbine: a numerical study on the effects of implementing long flange and Venturi holes

2022· article· en· W4302758075 on OpenAlexvenueno aff
Negin Maftouni, Yasaman Barghi

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsVenturi effectTurbineFlangeWind powerMarine engineeringRenewable energyWind speedEnvironmental scienceMechanical engineeringMechanicsEngineeringMeteorologyPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

There is a global interest in renewable energy resources such as wind energy. INVELOX (increased velocity) is an innovative ducted wind turbine system. Some research has been performed to improve the efficiency of this machine by various geometrical modifications. Two ideas are presented and numerically analyzed in the present research to study their effects on the wind velocity when approaching the wind turbine, and consequently, on the wind turbine efficiency. In one of them, a relatively long flange is added to the original design, and in the other one, a pair of holes is considered in the venture to try to increase the mass flow rate of wind when reaching the turbine. Three various sizes of holes are studied here. The results reveal that using the long flange leads to an increment in the maximum wind velocity (about 15.45%) and more than 176% increase in the harvested power. In addition, the pair of holes in the Venturi with a diameter of 35 cm results in a slight improvement in the harvested power. For the first time, the turbine has been modeled inside the Venturi. A comparison between the results for the INVELOX, including the turbine, and that of the turbine in the free stream indicates that implementing INVELOX leads to 3.14 times more power generation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.331

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 head0.225
Teacher spread0.210 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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