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Record W2323902692 · doi:10.1061/40569(2001)406

Solute Dispersion and Transport in Pipes under Transient Hydraulic Conditions

2001· article· en· W2323902692 on OpenAlexaff
Cristóvão Vicente Scapulatempo Fernandes, Bryan Karney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransient (computer programming)Dispersion (optics)Environmental scienceMechanicsTransient analysisHydraulic machineryPetroleum engineeringGeotechnical engineeringMaterials scienceComputer scienceEngineeringTransient responseMechanical engineeringPhysicsElectrical engineeringOptics

Abstract

fetched live from OpenAlex

Solving or approximating the advection-diffusion/dispersion equation (ADE) is a challenging and important problem and has thus motivated a great deal of intense research. A specific complication arises from the nature of the governing partial differential equation: it is characterized by a hyperbolic non-dissipative advective transport term, a parabolic diffusive (dispersive) term and, possibly, an additional reaction/decay mechanism. In most pipeline applications, the numerical transport scheme is coupled to a steady or nearly steady hydraulic model. By contrast, this paper presents an implicit finite difference scheme for the solution of the advection-dispersion-reaction (ADR) superimposed on a full transient, method of characteristics (MOC) based, hydraulic solution. This contribution represents one of the first serious attempts to analyze the impact of water hammer conditions, and in particular fluid inertia and compressibility, on the evolution of water quality in pipe networks. The results reflect the potential of the method especially as a foundation for a more comprehensive water quality analysis in water distribution systems. The combined effect of advection, dispersion and reaction are evident at distinct stages in the modelling results.

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: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.204

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 head0.188
Teacher spread0.180 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

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