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Record W2079353425 · doi:10.1115/omae2009-79667

Numerical Modeling and Evaluation of Wave Energy Converters

2009· article· en· W2079353425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVolume 4: Ocean Engineering; Ocean Renewable Energy; Ocean Space Utilization, Parts A and B · 2009
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConvertersEnergy (signal processing)Absorption (acoustics)Power (physics)AcousticsFrequency domainDamperWave energy converterPhysicsLinear congruential generatorRange (aeronautics)MechanicsControl theory (sociology)EngineeringComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Wave energy converters use the motion of floating or submerged bodies to extract energy from the waves. Power absorption can be simulated using a simple linear damper with a resistance to motion which is proportional to velocity. Because of the interaction between energy production and motion, there will be an optimum rate of energy production for each wave frequency. Too much damping or too little damping can cause little energy produced. The wave absorption range also depends on the tuned frequency. In this paper, the maximum rates of energy absorption for submerged and floating wave energy converters are evaluated by employing the panel-free method for the motions of the converters in the frequency domain. A general expression for the wave power absorption is described. Numerical studies show that the optimal energy efficiencies of wave energy converters can be well predicted by employing the panel-free method for motion computations.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.206
Teacher spread0.186 · 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