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Record W7065814549

Evaluation of a numerical wave modelling tool for studying the overtopping of rubblemound breakwaters

2018· article· en· W7065814549 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBreakwaterCrestPhysical modellingWave heightScale (ratio)Coastal engineeringScale modelEngineering design process
DOInot available

Abstract

fetched live from OpenAlex

Wave overtopping of rubblemound breakwaters is a complex physical process which influences the functional efficiency and structural stability of the structure. The mean overtopping discharge resulting from design wave and water level conditions is often an important consideration affecting the selection of breakwater profile and crest height. The volume of water which passes over the crest of a breakwater depends on the structure geometry and composition, the nearshore bathymetry, the water level, and the seastate conditions. Physical modelling at large scale has traditionally provided a robust and reliable means to support the design of rubblemound breakwaters. Recent advancements in computational fluid dynamics and computing power have led to increasing efforts to use numerical modelling as a complementary tool to physical modelling for breakwater design applications. This paper compares measurements from a physical model study of rubblemound breakwaters conducted by the National Research Council of Canada (NRC) with numerical simulations produced by the numerical model IH2VOF. The skill of the IH2VOF model in predicting free-surface elevations and mean wave overtopping discharges is assessed. The comparisons are conducted for a range of seastate conditions, water levels, and breakwater geometries. The findings demonstrate that IH2VOF offers a viable tool to complement physical model testing for rubblemound breakwater design applications.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.063
GPT teacher head0.257
Teacher spread0.194 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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