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Record W4390269488 · doi:10.18280/i2m.220604

Hydraulic Behavior of Weir-Gate Structure with Rectangular Side Obstacle under Free and Submerged Flow Conditions

2023· article· en· W4390269488 on OpenAlexvenueno aff
Rafi M. Qasim, Ammar S. Dawood

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2023
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsWeirFree flowObstacleFlow (mathematics)Hydraulic structureMechanicsGeotechnical engineeringGeologyPhysicsLawGeography

Abstract

fetched live from OpenAlex

This research examines the behavior of a weir-gate hydraulic structure when subjected to the presence or absence of a side obstacle.The study focuses on two scenarios, free flow and submerged flow, and employs a side obstacle with dimensions of 15 cm in height, 30 cm in length, and 1 cm in thickness, located 30 cm downstream from the weir-gate structure.A correlation matrix is derived using measured and calculated data to establish the relationship between various variables, both dimensional and non-dimensional.The results show a clear correlation between Froude number and discharge coefficient, while a random trend is observed between discharge coefficient and Reynolds number.A proper trend is found between Reynolds number and Froude number at the downstream location.The discharge coefficient is significantly influenced by factors such as gate water depth, weir water head, and vertical distance between the weir and gate.Additionally, the study investigates the effect of average water depth at the downstream on discharge and flow velocity.Two models, linear regression and artificial neural network, are developed and tested to estimate the coefficient of discharge (Cd).The ANN model proves to be the best-fit model for all scenarios, with an MSE of 0.00013141 and an R-squared value of 0.99.The machine learning algorithms used in this study demonstrate an increase in prediction accuracy for Cd.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.769

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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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