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Record W4411023000 · doi:10.1016/j.weer.2025.100011

Transient torque and power number of a fluid agitator for direct wind thermal energy conversion

2025· article· en· W4411023000 on OpenAlexafffund
Navid Nazari, Xili Duan

Bibliographic record

VenueWind energy and engineering research. · 2025
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgitatorTransient (computer programming)TorqueWind powerPower (physics)MechanicsMarine engineeringEnvironmental scienceEngineeringPhysicsElectrical engineeringComputer scienceImpellerThermodynamics

Abstract

fetched live from OpenAlex

The present research explores a novel method for direct conversion of wind power to heat through fluid agitation for decentralized heating in remote or off-grid areas. An experimental setup was designed to study the torque and power characteristics of the fluid agitator. Two different impellers were tested under various dynamic conditions, including acceleration, deceleration, and sinusoidal speed variations. The results demonstrate that the agitator’s power number under transient conditions differs significantly from its steady-state values. This finding is crucial for properly matching the agitator to a wind turbine, ensuring efficient energy transfer. Moreover, it was found that the transient power number is more sensitive to acceleration rates than to deceleration. When operated under a sinusoidal speed profile, both the frequency and amplitude of the speed variation strongly influence system performance. Temperature measurements of the working fluid confirmed that applying a sinusoidal speed profile leads to higher heat generation compared to constant-speed operation. These findings provide valuable insights for designing practical wind-powered thermal systems.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.787

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 designNot applicable
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
Published2025
Admission routes2
Has abstractyes

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