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Record W2121777977 · doi:10.1260/2041-4196.3.2.221

Numerical Modeling of the Impact with Structures of Tsunami Bores Propagating on Dry and Wet Beds Using the SPH Method

2012· article· en· W2121777977 on OpenAlexaff
Philippe St-Germain, Ioan Nistor, Ronald D. Townsend

Bibliographic record

VenueInternational Journal of Protective Structures · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSmoothed-particle hydrodynamicsComputer simulationNumerical modelsElevation (ballistics)GeologyNumerical analysisMechanicsNumerical modelingGeotechnical engineeringField (mathematics)Structural engineeringEngineeringPhysicsMathematicsGeophysics

Abstract

fetched live from OpenAlex

In this paper, the simulation of the impact with structures of tsunami-like bores rapidly advancing on dry and wet beds is performed using a three-dimensional numerical model based on the Smoothed Particle Hydrodynamics (SPH) method. Firstly, to validate the numerical model, physical experiments are simulated and a quantitative comparison between experimental and numerical results is presented. Secondly, numerical results of the propagation of bores on dry and wet beds are compared to the analytical solution of the shallow water equations. Furthermore, the resulting time-histories of the pressures and net force acting on a square column and a vertical wall due to the impact of these bores are compared qualitatively. To better understand the development of the hydrodynamic forces, a detailed analysis of the velocity field and of the water surface elevation is also incorporated. This study is part of a comprehensive interdisciplinary research program whose purpose is to help develop design guidelines for tsunami-prone structures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.317
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2012
Admission routes1
Has abstractyes

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