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Record W2087776151 · doi:10.3826/jhr.2008.3015

A transient 2-D water quality model for pipeline systems

2008· article· en· W2087776151 on OpenAlexaff
Gholamreza Naser, Bryan Karney

Bibliographic record

VenueJournal of Hydraulic Research · 2008
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTurbulenceMechanicsAdvectionTransient (computer programming)Flow (mathematics)DiffusionPhysicsThermodynamicsComputer science

Abstract

fetched live from OpenAlex

This study develops a water quality model in a pipeline during transient conditions using a two-dimensional (2-D) approach including advection, diffusion, and reaction terms. More specifically, using a modifiedVardy-Hwang hydraulic model, a 2-D transient model for flow is coupled with a 2-D advection-diffusion-reaction model for chemical constituent concentration.A five-region turbulence model is used to compute turbulent shear stresses. Using a fixed grid method of characteristics, the hydraulic equations are integrated numerically to determine the velocity and pressure head. Then, an explicit/implicit finite difference method is used to integrate the advection-diffusion-reaction equation (ADRE). A reservoir-pipe-valve-reservoir system illustrates the 2-D behaviour of transient flowdue to a sudden valve opening and results are compared with a one-dimensional (1-D) counterpart. Although the system response using the two models is not dramatically different, the 2-D results do show some discernable increases in realism and insight over the 1-D model. Interestingly, the study also reveals that the Taylor model produces insufficient dispersion in a large-diameter pipe carrying a fully turbulent flow with large Reynolds number.

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)0.002
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 score0.736
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.162
GPT teacher head0.355
Teacher spread0.193 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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