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Record W2068687211 · doi:10.1115/ipc2012-90050

Semi-Empirical Correlation to Quantify the Effects of Pipe Diameter and Internal Surface Roughness on the Decompression Wave Speed in Natural Gas Mixtures

2012· article· en· W2068687211 on OpenAlexaff
K. K. Botros, Lorne Carlson, Brian Rothwell, Philip Venton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsPetroleum Technology Alliance CanadaNova Chemicals (Canada)
Fundersnot available
KeywordsMechanicsShock tubeMach numberDecompressionOutflowPressure gradientShock waveInternal pressureMaterials sciencePhysicsThermodynamicsMeteorologyComposite material

Abstract

fetched live from OpenAlex

GASDECOM is typically used in the design of gas pipelines for calculating decompression speed in connection with the Battelle two-curve method used throughout the pipeline industry for the control of propagating ductile fracture. GASDECOM idealizes the decompression process as isentropic and one-dimensional, taking no account of pipe wall frictional effects. Previous shock tube tests showed that decompression wave speeds in smaller diameter and rough pipes are consistently slower than those predicted by GASDECOM for the same conditions of mixture composition and initial pressure and temperature. Preliminary analysis based on perturbation theory and the fundamental momentum equation showed a correction term to be subtracted from the ‘ideal’ value of the decompression speed. One parameter in this correction term involves a dynamic spatial pressure gradient of the outflow at the rupture location. While this is difficult to obtain without a shock tube or actual rupture test, data from 14 shock tube tests, as well as from 14 full scale burst tests involving a variety of gas mixture compositions, were analyzed to quantify the variation of this pressure gradient with gas conditions and outflow Mach number. A semi-empirical relationship was found to correlate this pressure gradient parameter with two basic parameters representing the natural gas mixture, namely the molecular weight of the mixture and its higher heating value (HHV). For lean gas mixes, the semi-empirically obtained correlation was found to fit very well the experimentally determined decompression wave speed curve. For rich gas mixes, the correlation fits both branches of the curve; above and below the plateau pressure. This paper provides the basis for the derived semi-empirical correlation, and suggests a procedure (with examples) to correct the ‘ideal’ (frictionless) GASDECOM prediction to account for both the effects of pipe diameter and pipe internal wall surface roughness.

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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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
Published2012
Admission routes1
Has abstractyes

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