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Record W2975604987 · doi:10.1080/15435075.2019.1671410

Shallow water effect of tandem flapping foils on renewable energy production

2019· article· en· W2975604987 on OpenAlexaff
Maryam Pourmahdavi, Pengfei Liu

Bibliographic record

VenueInternational Journal of Green Energy · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTurbineComputational fluid dynamicsFlappingMarine engineeringEnvironmental scienceRenewable energyTandemFlow (mathematics)Water flowFOIL methodWaves and shallow waterMechanicsAerospace engineeringMaterials scienceEngineeringEnvironmental engineeringGeologyPhysicsElectrical engineeringOceanography

Abstract

fetched live from OpenAlex

Studies about the flapping foil hydrokinetics turbines, as a new method to extract energy from incoming flow field, have recently increased significantly. Studies on the effect of shallow water conditions on the performance of the turbine have not been seen so far. This study investigates the effect of working environment on the performance of flapping foil hydrokinetic turbine. The unsteady and incompressible flow around two flapping foils in tandem operate in shallow water is simulated using Computational Fluid Dynamic (CFD) method. The results of shallow water conditions are compared with deep-water case. It is observed that the shallow water condition heavily affects the performance of the system and the total efficiency decreases considerably. Particularly, when the kinematic parameters are optimum for performance, the total power extraction efficiency of the system for h0 = 6c, h0 = 3c and h0 = 1.5c has a reduction of 7.24, 8.5, and 10.14%, respectively, compared with deep-water case. The interaction between the boundary layer of the sea floor and the flapping foils was found to be the main factor of efficiency reduction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.366

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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 designBench or experimental
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

Citations14
Published2019
Admission routes1
Has abstractyes

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