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Record W4362575255 · doi:10.22215/etd/2023-15391

Wind Tunnel Testing to Evaluate Noise Emissions from a Small Wind Turbine

2023· dissertation· en· W4362575255 on OpenAlexaff
Victoria Asi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsWind tunnelTurbineWind powerAerodynamicsMarine engineeringNoise (video)AeroacousticsHypersonic wind tunnelEngineeringQUIETWind speedAerospace engineeringEnvironmental scienceMeteorologyElectrical engineeringComputer scienceSound pressurePhysicsTelecommunications

Abstract

fetched live from OpenAlex

The wind power industry has experienced fast and tremendous growth, globally, in recent years. Consequently, there is a growing interest in investigating aerodynamics and aeroacoustics characteristics of wind turbines and developing environmental standards aimed at decreasing noise levels in the design and manufacture of these machines. This study was carried out in the Carleton University Wind Induced Dynamic Laboratory (WInDLab) that was designed to evaluate and quantify noise levels emitted by a small horizontal axis wind turbine. This investigation provided the first-ever set of measurements carried out in the WInDLab following the successful installation of the wind turbine. The background acoustic data obtained for the wind tunnel showed that the wind tunnel is not quiet enough for measurements of the type undertaken. The wind tunnel would need to be silenced and acoustically treated to capture noise with low frequencies. Recommendations for further work have been provided.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.007

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 head0.040
GPT teacher head0.275
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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