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Record W4389584810 · doi:10.17118/11143/20874

Effect of the ice accretion on the aerodynamics of NACA 64(3)-618airfoil

2023· article· en· W4389584810 on OpenAlexaffabout
Zahra Maleksabet, Janusz A. Koziński, Ali Tarokh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsLakehead University
Fundersnot available
KeywordsAirfoilNACA airfoilAerodynamicsAerospace engineeringGeologyAeronauticsPhysicsMechanicsEngineeringReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Abstract: Wind energy is one of the biggest sources of renewable energy in Canada that accounts for 3.5 percent of electricity generation. However, icing on the blades during the cold seasons can cause some serious problems. This can apply extra inertia and makes the rotary system unbalanced which can damage the turbine. Furthermore, it changes the aerodynamic shape of the blades and changes the lift and drag coefficient of the wind turbine blades which reduces the generated power. To study the effects of the ice layer growth on the aerodynamic behavior of the blade, a 2D airfoil of NACA 64(3)-618 which is a common airfoil used in wind turbines is considered. In this study, the turbulent flow over the airfoil with an iced layer on the leading edge is modeled. The k-? SST model is used to simulate the turbulent flow and OpenFOAM CFD package is utilized to integrate the governing equations. The flow behavior around the airfoil with and without the icing layers is studied for the Re equal to 137,000 when the angle of attack is changed from 0 to 15 degree. It is expected that according to the changes in the airfoil shape in the presence of the icing layer which is a destructive phenomenon, the lift coefficient decreases and the drag coefficient increase. Also, it is expected that icing can affect the separation point and advances it.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.148

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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