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Record W2395498647 · doi:10.1061/9780784479889.041

Physical Scale and Computational Modeling in the Development of a Vortex-Type Stormwater Retention Pond

2016· article· en· W2395498647 on OpenAlexafffund
Rezaul Chowdhury, M.S. Ahadi, Kerry A. Mazurek, Gordon Putz, Donald J. Bergstrom, Cory Albers

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2016 · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Environmental Protection Agency
KeywordsBermInletDeposition (geology)SedimentComputational fluid dynamicsEnvironmental scienceResidence time (fluid dynamics)Flow (mathematics)Hydrology (agriculture)FluentStormwaterVortexGeologySurface runoffGeotechnical engineeringEngineeringMeteorologyGeomorphologyMechanicsGeography

Abstract

fetched live from OpenAlex

This paper presents results from a study to examine the flow and sediment deposition patterns in a new vortex-type stormwater retention pond using physical scale and computational models. The pond, that is circular in plan, has a peripheral inlet and a central outlet, which creates a strong circulation in the pond. Two geometries of the pond were tested. Measurements of the residence time distributions, flow patterns, and sediment deposition patterns were taken at the design flows for the two pond geometries. The effectiveness of an internal berm to improve desirable flow characteristics was also examined. The berm improved flow and sediment deposition in the pond, as the sediment deposition patterns showed that most of the sediments deposited outside the berm. This is considered to be beneficial for pond maintenance. The physical scale model results are also compared to 3D computational fluid dynamics (CFD) modeling results using ANSYS Fluent.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.324

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 head0.015
GPT teacher head0.195
Teacher spread0.181 · 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
Published2016
Admission routes2
Has abstractyes

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