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Record W3005909105 · doi:10.1139/cjce-2019-0287

Stability evaluation of overtopped hydraulic structures using computational fluid dynamics

2020· article· en· W3005909105 on OpenAlexafffundvenueabout
Mario Freitas, Étienne Favre, Pierre Léger, Lineu José Pedroso

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsPolytechnique Montréal
FundersGlobal Affairs CanadaNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSpillwayGravity damGeologyComputational fluid dynamicsStability (learning theory)Geotechnical engineeringFlow (mathematics)EngineeringMechanicsStructural engineeringFinite element methodComputer science

Abstract

fetched live from OpenAlex

A particularly challenging aspect in gravity dam stability assessment is the estimation of the induced hydrodynamic water pressure when water with significant velocity is overtopping gravity dams and flowing in or over spillway components. The water flow conditions, including the related pressure fields and resultant forces, are difficult to quantify accurately. Herein, existing dam safety guidelines to estimate the weight of the overflowing water nappe on gravity dams with rectangular crests are first reviewed. Then, a computational fluid dynamics (CFD) methodology is developed to improve the simplified estimation of hydrodynamic pressure fields acting on the rectangular crests of submerged gravity dams. The CFD pressures are used as input data for the classical structural stability analyses based on the gravity method to more adequately quantify the dam stability during overtopping. A back analysis is also performed on the stability of an existing gated spillway that was overtopped during the 1996 Saguenay flood in Québec.

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 score0.102
Threshold uncertainty score0.664

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 head0.023
GPT teacher head0.217
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

Citations4
Published2020
Admission routes4
Has abstractyes

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