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Record W2269897038

SPH Modeling of Extreme Hydrodynamic Forces on Slender Structures

2011· article· en· W2269897038 on OpenAlexaboutno aff
Philippe St-Germain, Ioan Nistor, Dan Palermo, R. D. Townsend

Bibliographic record

VenueProceedings of the 34th World Congress of the International Association for Hydro- Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSmoothed-particle hydrodynamicsHydraulic jumpGeologyJoint (building)Hydraulic structureGeotechnical engineeringNumerical modelsFlash floodMarine engineeringEngineeringMechanicsNumerical modelingStructural engineeringFlow (mathematics)PhysicsGeographyGeophysicsFlood myth
DOInot available

Abstract

fetched live from OpenAlex

A three-dimensional Smoothed Particle Hydrodynamics (SPH) numerical model was used to simulate high-velocity bore impacts on structures that can occur during extreme events such as tsunamis, torrential flash floods and dam-break floods. Numerical simulations of the impact of hydraulic bores of varying heights on a slender circular structure are performed. Resulting forces, along with the time-dependent pressure distributions are compared with large-scale experimental results obtained by the authors as part of a comprehensive experimental program conducted jointly with the Canadian Hydraulic Centre at the National Research Council, Canada. This study is a component of a wider scope joint hydraulic and structural engineering research project, whose final purpose is to improve design guidelines for inland structures located in coastal regions prone to tsunami waves attack. The results of this study are also applicable to other types of extreme hydrodynamic impacts and loading such as flash floods and dam-break waves.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.512

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 head0.027
GPT teacher head0.223
Teacher spread0.195 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the 34th World Congress of the International Association for Hydro- Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water EngineeringSame topicFluid Dynamics Simulations and InteractionsFrench-language works237,207