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Record W3025156756 · doi:10.1061/9780784482971.023

The Impacts of Time Integration Schemes on the Pressure Surge Prediction in a Closed Conduit Transient Flow

2020· article· en· W3025156756 on OpenAlexaff
Arman Rokhzadi, Musandji Fuamba

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2020 · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsElectrical conduitSurgeTransient (computer programming)Transient flowTransient analysisComputer scienceFlow (mathematics)MechanicsEnvironmental scienceGeologyMarine engineeringTransient responseEngineeringPhysicsElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

The pressure surge prediction in a closed conduit partially pressurized transient flow has been investigated following suddenly blocking the pipe downstream end. In a shock-fitting approach, the rigid column model as well as the Saint-Venant equations set and the ideal gas law for air pocket variations were used for the computations. The aim was to examine the performance of the shock-fitting approach and possible effects of the explicit and implicit time integration schemes on the quality of the solutions. It was found that the shock-fitting approach can improve the quality of the solutions compared to the rigid column model provided that implicit schemes are used for time integrations. The main reason is due to more dissipative property of implicit schemes, compared to explicit schemes, by which the extra kinetic energy associated with the approximation of the models can be dissipated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.152
Teacher spread0.147 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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