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Record W2015417521 · doi:10.14796/jwmm.c370

Modeling the Motion and Spread of Air Pockets within Stormwater Sewers

2014· article· en· W2015417521 on OpenAlexvenueno aff
Thomas M. Hatcher, Carmen D. Chosie, José G. Vasconcelos

Bibliographic record

VenueJournal of Water Management Modeling · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
FundersAuburn University
KeywordsSanitary sewerStormwaterStormwater managementEnvironmental scienceCombined sewerEnvironmental engineeringSurface runoff

Abstract

fetched live from OpenAlex

There are important adverse effects linked to the presence of entrapped air pockets in stormwater systems. These effects include loss of conveyance, surging caused by air compression, loss of storage, and geysering. The capability to monitor the formation and motion of entrapped air pockets is highly desirable when modeling extreme rain events in stormwater systems. Experimental investigations conducted at Auburn University have led to a better understanding of important flow features related to the motion of entrapped air in closed conduit flows. Results from these studies, which are summarized in this work, have supported the development of an innovative approach to perform simulations of the air pocket motion based on Benjamin's (1968) work on air cavity motion. The proposed model accounts for surface tension in a similar manner to Wilkinson (1982) but incorporates background flows and circular cross-sections. Air motion is described with a non-Boussinesq integral gravity current model approach, assuming a steady state flow for the water. Measured and predicted values for the air pocket leading edge coordinate and celerity are compared. Results indicate that the proposed model is fairly accurate and may constitute an efficient model to describe entrapped air pocket kinematics in closed conduit flows.

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)0.001
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 score0.383
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.190
Teacher spread0.182 · 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
Published2014
Admission routes1
Has abstractyes

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