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Record W2034060451 · doi:10.1139/l07-141

Jet impact geometry and plunge pool dimensions effects on dynamic pressures at pool sidewalls

2008· article· en· W2034060451 on OpenAlexvenueno aff
Seyed Mehdi Borghei, Pouyan Zarnani

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsJet (fluid)Impact pressureMechanicsDynamic pressureCore (optical fiber)Head (geology)Materials scienceGeologyPhysics

Abstract

fetched live from OpenAlex

A plunge pool is one of the energy dissipater structures for high head dams, which ensures stability of the dam under extreme hydrodynamic pressure, due to falling jet impacts. The excess energy of the jet is dissipated, together with significant impact pressure exerted on the floor and pool walls. Thus, appropriate assessment of jet dynamic characteristics in the plunge pool is essential. This research presents experimental results focusing on the distribution of mean and extreme pressure fluctuations on the sidewalls, due to circular and rectangular plunging jets, and the effects of pool dimension, especially in narrow valleys. The experimental variables are discharge, geometry of jet, pool depth, pool width, and sidewall slope. Hydrodynamic pressure on the sidewalls due to decreasing bottom width from 6 to 3 times the jet impact diameter, or width, is quite different for core and developed jets. For core jets, it increases the maximum pressure, while decreasing the minimum and mean dynamic pressure on the sidewalls, while, in cases of developed jet impact, a reverse effect is observed for mean dynamic pressure. Finally, the results of pressure variation on plunge pool walls for different jet geometry and pool dimensions are presented graphically and design suggestions for pool optimum dimensions are provided.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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
Published2008
Admission routes1
Has abstractyes

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