MétaCan
Menu
Back to cohort
Record W2324533366 · doi:10.1061/9780784413692.075

A Nonoscillatory Shock Capturing-Based Numerical Model for Calculating Highly Transient Mixed Flow in Pipelines

2014· article· en· W2324533366 on OpenAlexaff
Ahmad Malekpour, Bryan Karney

Bibliographic record

VenuePipelines 2014 · 2014
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransient (computer programming)Godunov's schemeFlow (mathematics)ViscositySolverMechanicsRiemann solverRoe solverShock (circulatory)Computer simulationMomentum (technical analysis)Shock waveNumerical analysisComputer scienceApplied mathematicsMathematicsMathematical optimizationPhysicsMathematical analysisThermodynamics

Abstract

fetched live from OpenAlex

This paper presents a nonoscillatory model based upon the two-component pressure approach (TPA) model, which is utilized for calculating transient mixed flow in pipelines. The proposed model uses a first-order Godunov numerical scheme with an approximate HLL Riemann solution. It is shown that to remove the numerical oscillations the numerical viscosity of the scheme should be enhanced, particularly once the flow transition changes the regime from open channel to pressurized flow. To accomplish this goal an algorithm is proposed to improve the wave structure of the numerical scheme based upon which the HLL solver estimates the mass and momentum fluxes. The proposed algorithm significantly increases the scheme's numerical viscosity whenever flow transitions tend to occur, while it otherwise admits an optimum numerical viscosity. The performance of the model is numerically explored and verified through several test cases.

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 categoriesMeta-epidemiology (narrow)
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 score0.742
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePipelines 2014Same topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207