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Record W4389584773 · doi:10.17118/11143/20931

Flutter analysis of vertical-axis wind turbine blades using a simplifiedaeroelastic model

2023· article· en· W4389584773 on OpenAlexaff
Mohadeseh Mokhtari, Mojtaba Kheiri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsAeroelasticityFlutterTurbineAerospace engineeringTurbine bladeGeologyVertical axisAerodynamicsHorizontal axisWind powerMarine engineeringComputer scienceStructural engineeringEngineeringEngineering drawingElectrical engineering

Abstract

fetched live from OpenAlex

This study investigates the aeroelastic flutter of a vertical-axis wind turbine (VAWT) blade using a two-degree-of-freedom typical section model and the unsteady aerodynamic theory.The long-term goal of this study is to develop fast, reliable analytical models for use in aeroelastic design optimization of large-scale floating offshore VAWTs.There has been renewed interest in VAWTs in recent decades since they are considered as a viable option for offshore wind power generation.Like other lifting surfaces, the blades of a VAWT are prone to aeroelastic flutter.Few analytical models were previously developed for the aeroelastic stability analysis of VAWTs, but virtually all used a quasi-steady aerodynamic theory in their numerical analysis.Studies on the aeroelasticity of aircraft wings have proven the inadequacy of the quasi-steady aerodynamic theory.Also, to the best of the authors' knowledge, no comprehensive parametric studies (including the effects of mass ratio, frequency ratio, and the offset between the center of mass and the elastic axis) have been made on the aeroelastic flutter of VAWTs blades.The need for such studies is felt even more now as VAWTs with lighter and longer composite blades are being considered for large-scale 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 score0.049
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.036
GPT teacher head0.267
Teacher spread0.231 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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