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Record W2399698128 · doi:10.1061/9780784479872.020

Algebraic Water Hammer: Global Formulation for Simulating Transient Pipe Network Hydraulics

2016· article· en· W2399698128 on OpenAlexaff
J. D. Nault, Bryan Karney, Bok Ki Jung

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWater hammerHydraulicsReflection (computer programming)Transient (computer programming)Pipe network analysisPipe flowBoundary value problemFlow (mathematics)HammerBoundary (topology)Algebraic numberMethod of characteristicsComputer scienceMechanicsMathematicsMathematical analysisEngineeringMechanical engineeringPhysicsDifferential equation

Abstract

fetched live from OpenAlex

Algebraic water hammer (AWH) represents a simplified and more compact form of the classical method of characteristics (MOC) for solving the 1-D equations of unsteady-compressible pressurized flow. Unlike the MOC, AWH focuses on the transmission and reflection of waves at nodal boundaries. The characteristic expressions are extended over multiple pipe reaches (with fewer variables), so they are conveniently summarized in a system equation using incidence matrices; accordingly, global AWH (GAWH) is readily implemented and well-suited to the analysis of large pipe networks. Additionally, the solution of complex and coupled boundary conditions does not require lengthy mathematical formulae, for they are inherently resolved within the formulation. To compensate for the loss of spatial resolution, a flexible scheme is proposed to maintain accuracy for wave attenuation. The current work is demonstrated using a small looped pipe network. Simulation results are compared against those from an MOC-based model, and it is shown that both yield nearly identical 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.487

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 head0.005
GPT teacher head0.170
Teacher spread0.165 · 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 designNot applicable
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

Citations7
Published2016
Admission routes1
Has abstractyes

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