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Record W4379877348 · doi:10.2514/6.2023-3641

Numerical investigation of aerodynamics and aeroacoustics of helical Darrieus wind turbines

2023· article· en· W4379877348 on OpenAlexaff
Kartik Venkatraman, Stéphane Moreau, Julien Christophe, Christophe Schram

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsWakeAerodynamicsTurbineVortexAeroacousticsLarge eddy simulationMechanicsAcousticsNoise (video)Trailing edgeStructural engineeringPhysicsEngineeringSound pressureAerospace engineeringTurbulenceComputer science

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-3641.vid The aerodynamic and aeroacoustic characteristics of three scaled model Helical-Darrieus vertical axis wind turbines (VAWT) with helix angles of 45°, 60°, and 120° are examined at a fixed rotational speed of 3500 rpm. A 3D Lattice Boltzmann/Very Large Eddy Simulation is performed using the commercial flow solver PowerFLOW. The predicted power coefficient for the helix 60° turbine demonstrates good agreement with available experimental data. A decrease in blade loading is observed with increasing helix angles. The helical twist generates a mean flow in the spanwise direction, leading to asymmetry in blade loading and wall pressure spectra along the span. The primary flow structures involve tip vortices and their interaction with end plates. Additionally, the helix twist aids in reducing blade wake interaction, as shed structures do not directly impinge on the incoming blade. The flow solution is then also coupled with an in-house aeroacoustic propagation code, SherFWH, based on the Ffowcs Williams and Hawkings analogy for far-field noise propagation. The present numerical predictions for noise at an observer location show excellent agreement with available experimental data for the helix 45° turbine. The leading section of the blade exhibits high wall pressure fluctuations over the azimuthal revolution of the turbine, forming the dominant noise contribution at the blade passing frequencies (BPF), while smaller structures at the blade-end plate junction also contribute to high-frequency broadband noise. As the helix angle increases, both the tonal peaks at the BPFs and the broadband noise decrease due to enhanced flow mixing and three dimensional flow. A 20 dB decrease is seen at the first BPF tonal peak for the helix 120° turbine. Moreover, the helix angle also alters the orientation of the noise directivity, aligning it along the helix angle.

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 score0.129
Threshold uncertainty score0.453

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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