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Record W3154883084 · doi:10.1139/tcsme-2020-0194

The effect of drag force on the body frequencies and the power spectrum of a bladeless wind turbine

2021· article· en· W3154883084 on OpenAlexvenueno aff
Negin Maftouni, Mahsa Dehghan Manshadi, Seyed Milad Mousavi

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTurbinePhysicsVibrationDragMechanicsVortex sheddingAcousticsTurbulence

Abstract

fetched live from OpenAlex

New types of bladeless wind turbines and electricity generation are currently one of the most interesting topics in engineering. Electricity generation using structural vibration due to the resonance phenomenon is the concept behind a vortex bladeless turbine. The present study numerically investigated the effects of the drag force on the body frequency of an oscillating bladeless wind turbine. A two-dimensional numerical simulation was performed for a cylinder with a semi-circular cross-flow cross-section in two different cases. This research was conducted for both uncontrolled and controlled oscillating cylinders. The controlling process was performed using a pair of ring magnets as springs with a variable coefficient. The flow field, vibration, vortex shedding, structural frequencies, and resonance phenomena were studied in this research. Finally, the controlled and uncontrolled frequencies of the cylinder are explored, and the power spectra for various velocities are analyzed in two different states, namely, with and without a tuning system. From the results, it can be concluded that the usage of the controlling system in these turbines can significantly regulate the oscillations and increase the frequency value by limiting the vibration amplitude. According to this principle, it can be inferred that increasing the frequency of fluctuations greatly increases the production capacity of these turbines.

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 score0.448
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.003
GPT teacher head0.172
Teacher spread0.169 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicFluid Dynamics and Vibration AnalysisFrench-language works237,207