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Record W2316670093 · doi:10.2514/6.2008-1333

Performance Analysis of a Smaller-Capacity Straight-Bladed VAWT with Prospective Airfoils

2008· article· en· W2316670093 on OpenAlexaff
Mazharul Islam, Mohammad Ruhul Amin, David S.‐K. Ting, Amir Fartaj

Bibliographic record

Venue46th AIAA Aerospace Sciences Meeting and Exhibit · 2008
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAirfoilAerodynamicsWind powerMarine engineeringNACA airfoilAerospace engineeringLift (data mining)Vertical axis wind turbineTurbineRelative windComputational fluid dynamicsComputer scienceCascadeEngineeringAngle of attackPhysicsMeteorologyTurbulenceReynolds numberElectrical engineering

Abstract

fetched live from OpenAlex

Modern wind turbines are categorized as horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs), which are currently being utilized for diversified applications. The basic theoretical advantages of VAWTs are: (i) they accept the wind from any direction, and (ii) the generator, gearbox etc. can be placed on the ground. Selection of airfoil is one of the most critical factor in achieving optimum aerodynamic performance and in determining the optimum dimensions of a fixed-pitch straight-bladed vertical axis wind turbine (SB-VAWT). Airfoil related design changes also have the potential for increasing the cost effectiveness of VAWTs. Most of the earlier research works carried out by different research organizations mainly used NACA symmetric airfoils which were unable to self-start properly. In this paper, detail systematic investigative analyses have been performed with high-lift asymmetric airfoils appropriate for self-starting and better performance of smaller capacity SB-VAWT. In order to do the performance analysis, a computational scheme has been developed using the Cascade Model and XFOIL, a sub-sonic airfoil design and analysis tool developed in MIT. It has been found out that the results obtained from the computational scheme conform reasonably well with the experimental results. Subsequently, three prospective airfoils have been identified using this computational scheme. It has been found that their performance is better than conventionally used NACA 0015 at low tip speed ratio range where the problem of self-starting happens. Copyright © 2008 by Islam, Amin, Ting & Fartaj.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.201
Teacher spread0.179 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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