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Record W2003306506 · doi:10.1139/l04-021

Potential flow solution for a free surface flow past a sudden slope change

2004· article· en· W2003306506 on OpenAlexfundvenueno aff
Amir Reza Zarrati, Yee‐Chung Jin, Ahmad Shanehsazzadeh, F Ahadi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFree surfaceMechanicsFlow (mathematics)SpillwayOpen-channel flowGeologyGeotechnical engineeringHydrostatic equilibriumPhysics

Abstract

fetched live from OpenAlex

An analytical model was developed to calculate the pressure distribution in a free surface flow past a sudden change in channel slope. A conformal transformation technique was used to solve the problem analytically in a way that there is no need for trial and error to find the location of the free surface. Two methods were employed for this simulation: flow at a corner and free streamline theory. It was shown that free streamline theory is more accurate. Experiments were conducted to verify the ability of the analytical model to calculate the pressure distribution in a channel with a sudden change in slope. Slope changes of 6.22°, 10°, and 15° were tested with various flow discharges. The analytical model predictions of pressure distribution along the channel bed and with depth agreed well with the experimental measurements. Pressures up to 25 times the hydrostatic pressure were experimentally measured near the point of sudden change in slope. These pressures were reproduced by the model. The analytical model predictions of the water surface profile over a ramp in a prototype spillway were compared with those of a numerical model. The comparison showed a good agreement.Key words: pressure distribution, free surface flow, analytical model, chute spillway, aerator ramp, potential flow.

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: none
Teacher disagreement score0.900
Threshold uncertainty score0.953

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 head0.008
GPT teacher head0.173
Teacher spread0.165 · 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

Citations3
Published2004
Admission routes2
Has abstractyes

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