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Record W2948649311 · doi:10.11159/ffhmt19.114

Numerical study of wave attenuation by mass of rising bubbles

2019· article· en· W2948649311 on OpenAlexvenueno aff
Mojtaba Shegeft, Madjid Ghodsi Hassanabad, Mojtaba Ezam

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAttenuationPhysicsGeologyMechanicsComputational physicsAcousticsOptics

Abstract

fetched live from OpenAlex

This paper describes a numerical study on the performance of the air bubble breakwater to attenuating wave energy using 2D CFD modelling. This numerical study gives reasonable results which illustrate CFD modelling is one of the best ways to have a good prediction of wave attenuation. Pneumatic breakwater is installed under the sea level, so it is hidden and it does not have a negative impact on shipping. Regarding the advantages of the breakwater and its good compatibility with environment and its high performance as a mobile breakwater, further study in this field is necessary. In the model, Finite volume method has been used to analyse air bubble breakwater, the continuity and momentum equations were selected as the governing equations. Shear Stress Transport (SST) k-omega turbulence model is implemented to the Reynolds Stresses in RANS equations due to the high amount of turbulence in this system. Also, interaction between water and air was analysed with the Volume of Fraction (VOF) method. Finally, according to the numerical models, the Wave attenuation depends on the wavelength and the air discharges from the orifices and there is a positive correlation between the mass of rising bubbles and wave attenuation with constant steepness. Besides, the experimental results and the numerical results of this research were in good accordance. Therefore, it can be concluded that the present numerical method is convenient for pneumatic breakwater modelling.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.360

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 head0.017
GPT teacher head0.215
Teacher spread0.198 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicCoastal and Marine DynamicsFrench-language works237,207