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Record W4400982384 · doi:10.14796/jwmm.c518

Verification of Three-dimensional Numerical Study of Scour in Channel, Sudden and Gradual Contractions using Experimental Data

2024· article· en· W4400982384 on OpenAlexvenueno aff
Soheil Abbasi, Hossein Samadi Bruojeni, Shohreh Bahrami, Marjan Parsmehr, Reza Barati

Bibliographic record

VenueJournal of Water Management Modeling · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsChannel (broadcasting)GeologyMechanicsGeotechnical engineeringComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a verification of a numerical and experimental simulation of scour patterns at channel contractions using a 3-dimensional SSIIM (Sediment Simulation in Intakes with Multiblock option) model and laboratory tests. For this purpose, two states of sudden angle¬-90° and gradual angle -50° contraction were simulated. The numerical model was calibrated and verified using the laboratory data. The accuracy of the model was calculated as 0.936 based on the Nash-Sutcliffe model efficiency coefficient, and 10.18% based on the mean relative error. Results showed that around 80% of scouring occurred during the first 20% of the equilibrium time. Also, it was concluded that the maximum rate of scouring occurred during the first hours of experiments and computations, and decreased with time. The results showed that the average scour rate for the sudden contraction state was 29.5% greater than the gradual state, which indicated a positive impact of gradualness of conversion in reducing maximum scour depth. This is an appropriate performance of the numerical model to simulate the scour pattern in channel contraction.

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 score0.040
Threshold uncertainty score0.257

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 head0.057
GPT teacher head0.290
Teacher spread0.233 · 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
Published2024
Admission routes1
Has abstractyes

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